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MARK2052-无代写
时间：2023-10-11
MARK2052 – Marketing Research
Tutorial 4, Week 5
School of Marketing
UNSW Business School
Today…
• Group Allocations
• Section 2
• T-Tests
• Section 3
• T-Tests
• Extra Questions
Section ID Name Group
W11B 5421143 Patrick Chong,Patrick Jet Yao 1
W11B 5418614 Lin Ding,Xiao Lin 1
W11B 5418108 Sabrina Du,Sabrina Laurus 1
W11B 5418518 Teh,Brielle Lee Ann 1
W11B 5421479 Aryan Zaman,Aryan 1
W11B 5361467 Renae Bennett,Renae Paris Mary 2
W11B 5257350 Des Georgaklis,Despina-Marie 2
W11B 5359672 Jayden Simmons,Jayden 2
W11B 5367149 Sandy Sithisakd,Sandy 2
W11B 5401122 Saharsha Tuladhar,Saharsha Dhan 2
W11B 5424607 Deveka Lingam,Deveka 3
W11B 5418278 Mridula Tirumala,Mridula 3
W11B 5421635 Williams,Ava Anais 3
W11B 5509250 Katelyn Wong,Katelyn Xiu En 3
W11B 5422371 Nikki Zhang,Nikki 3
W13B 5420926 Bhatti,Humnah 4
W11B 5416266 Elyssa Chun,Elyssa Grace 4
W11B 5416601 Proteek Huq,Proteek 4
W11B 5417489 Alicia Mu,Alicia Yang Foon 4
W11B 5417429 Britney Truong,Britney 4
W11B 5341949 Camille Chen,Miaoyang ?
W11B 5425140 Yang Chen,Zhouyang Yang ?
Section ID Name Group
W13B 5376290 Haniya Huang,Ya 1
W13B 5438204 Lyric Li,Pengyu 1
W13B 5416542 Vy Nguyen,Vy-Thuc 1
W13B 5422231 Isabella Purnell,Isabella Faith 1
W13B 5354579 Irene Xu,Jingwen 1
W13B 5361655 Elia Berelekhis,Elia 2
W13B 5506861 Chloe Chan,Chloe Jingyi 2
W13B 5348010 Jessica Simon,Jessica 2
W13B 5391681 Rohin Somani,Rohin Munir 2
W13B 5433472 Maggie Yu,Miao 2
W13B 5360619 Deon Hales,Deon Jay Fairley 3
W13B 5359137 Warren Lu,Warren Ding Sheng 3
W13B 5523340 Krishang Nair,Krishang Raakesh 3
W13B 5394819 Kiyah Spinetti,Kiyah Jordan 3
W13B 5366259 Faye Wang,Jianzhe ?
W13B 5402555 Sihan Wang,Sihan ?
4With the:
• Content?
• Tutorials?
• Tests?
• SPSS?
5Group Project Admin
• Excel Sheet Link will be uploaded to our MS Teams File Section / Emailed.
• Tips:
• Create a group chat (MS Teams, Socials).
• Weekly meetings.
• Weekly duties delegated with deadlines.
• Weekly check ups.
6What is a t-test?
• T-test: type of inferential statistics used to determine if there is a significant difference between the
means of two groups.
• A large t-score tells you that the groups are different.
• A small t-score tells you that the groups are similar.
• One variable must be continuous, and the other categorical.
• 3 Types:
• One Sample t-test
• Independent Samples t-test
• Paired Samples t-test
7Types of t-tests
• One Sample t-test: tests the mean of a single group against a known mean.
• Making a conclusion about the population mean compared to a given level.
• We have one question, and one segmentation.
• E.g. a product will be introduced if the population mean performance rating μ is
greater than 7 on a 10-point scale.
• HA: The population mean performance rating is greater than 7 on a 10-point scale.
• H0: The population mean performance rating is less than or equal to 7 on a 10-point
scale.
H0: < 7.0
µ> 7.0
µ
HA:
8Types of t-tests
• Independent Samples t-test: compares the means for two independent groups
• One variable must be continuous, other must be of categorical nature with two independent groups.
• We have one question, and two segmentations.
• E.g. do males and females differ in their likelihood to cook from scratch?
• H0: The average likelihood (i.e. population mean) of men cooking dinner from scratch is
equal to the average likelihood of women cooking dinner from scratch.
• HA: The average likelihood (i.e. population mean) of men cooking dinner from scratch is
not equal to the average likelihood of women cooking dinner from scratch.
µµ 210 : =H
µµ 211 : ¹H
9Independent Samples T-test
• Levene’s Test for Equality of Variances: tests whether the variances of two samples are approximately equal.
• An assumption of t-tests is that variances of the two samples are equal.
• F-test: a procedure used to determine whether there is more variability in the scores of one sample than in the
scores of the other sample.
• Null Hypothesis: the groups we’re comparing all have equal population variances.
• Alternate Hypothesis: the groups we’re comparing do not have equal population variances.
• ROT: If Sig. (P-Value) is greater than 0.05, Levene’s Test is non-significant, so equal variances assumed.
• Interpret T-test using the top row.
• ROT: If Sig. (P-Value) is less than 0.05, Levene’s Test is significant, so equal variances are not assumed.
• Interpret T-test using bottom row.
10
Types of t-tests
• Paired Sample t-test: two sets of observations from the same respondents (e.g. one year).
• Two questions, one segmentation.
• E.g. How much did you like UNSW when you were 18? And, what about now?
• HA: The amount which I liked UNSW when I was 18 is the not equal to the amount which I
like UNSW now.
• H0: The amount which I liked UNSW when I was 18 is the exact same as the amount which
I like UNSW now.
H0: µD = 0
H1: µD ¹ 0
11
13
Section 2.2.3
• Test 3 : Men and women do not differ on their requirements of power from the car.
• Alternative hypothesis
• HA:
• Null hypothesis
• H0:
14
Section 2.2.3
• Test 3 : Men and women do not differ on their requirements of power from the car.
• Independent Samples t-test
• Alternative hypothesis
• HA: µ men power ≠ µ women power
• The average likelihood (i.e. population mean) of power being a requirement from the car for men
is not equal to the average likelihood of power being a requirement from the car for women.
• Null hypothesis
• H0: µ men power = µ women power
• The average likelihood (i.e. population mean) of power being a requirement from the car for men
is equal to the average likelihood of power being a requirement from the car for women.
15
Section 2.2.3 Independent-Samples t-test
• Click Analyse
• Click Compare Means
• Click Independence-Samples T Test…
• Input test variable (e.g. Car attribute – power [power])
• Input Grouping Variable (e.g. sex of respondent)
• Select Define Groups…
• Select Use specified values
• Group 1: (e.g. 0 = females)
• Group 2: (e.g. 1 = males)
• Click Continue
• Click OK
16
Section 2.2.3 Independent-Samples t-test
17
Section 2.2.3 Independent-Samples t-test
1. Look at Levene’s Test for Equality of Variances
i. If Levene’s test of significance is not significant
i.e. greater than 0.05, we can assume variances
are not significantly different i.e. “equal variance
assumed”. Therefore, we use the output from the
first row.
18
Section 2.2.4
• Test 4 : People usually rate the importance of security at a 5.
• Alternative hypothesis
• HA
• Null hypothesis
• H0:
19
Section 2.2.4
• Test 4 : People usually rate the importance of security at a 5.
• One sample t-test
• Alternative hypothesis
• HA: µ>5
• On average, people care about the importance security.
• Null hypothesis
• H0: µ≤5
• On average, people somewhat or do not care, or are indifferent to the importance of
security.
20
One-Sample T-Test in SPSS
• Click Analyse
• Click Compare Means
• Click One-Sample T Test…
• Input Test Variable (e.g. Value – security)
• Input Test Value (e.g. 5)
• Click OK
• https://statistics.laerd.com/spss-tutorials/one-sample-t-test-using-spss-statistics.php
21
Section 2.2.4
• Test 4: People usually rate the importance of security at a 5.
• Remember, if p is less than 0.05, we accept HA.
• P = 0.000
• ∴ We accept that on average, people
care about the importance security.
• Mean = 5.76
22
Section 3 - Questions on T-Tests
• By the time you are done with this page, you should be able to tell when to use the three
different types of t-tests.
• If you are still not clear, please let me know and we’ll go over these again J
23
Section 3 - Questions on T-Tests
• For the following identify which type of t-test is applicable, write down the null and hypotheses,
and then perform the test in SPSS:
1. High gas mileage and speed are not given the same importance by respondents.
2. High gas mileage and speed are given the same importance by respondents.
3. The importance of high mileage does not differ between men and women.
4. Men do not care about the mileage of the car.
24
Section 3.1 - Identify which test is appropriate.
1. High gas mileage and speed are not given the same importance by respondents.
One sample t-test Independent Samples t-test Paired sample t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Sample t-test: two sets of observations from the same respondents (e.g. one year)
25
Section 3.1 - Identify which test is appropriate.
1. High gas mileage and speed are not given the same importance by respondents.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents
26
Section 3.2 - Identify which test is appropriate.
1. High gas mileage and speed are given the same importance by respondents.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents
27
Section 3.2 - Identify which test is appropriate.
1. High gas mileage and speed are given the same importance by respondents.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean.
• Independent Sample t-test: compares the means for two groups.
• Paired Samples t-test: two sets of observations from the same respondents.
28
Section 3.2 - Write down the null hypothesis and the alternate hypothesis.
1. High gas mileage and speed are given the same importance by respondents.
Paired Samples t-test
• Alternate Hypothesis
• HA : µ mileage - µ speed ≠ 0
• [ or µ mileage ≠ µ speed ]
• HA: On average, there is a difference in the way respondents rate the importance of speed and gas mileage.
• Null Hypothesis
• H0: µ mileage - µ speed = 0
• [ or µ mileage = µ speed ]
• H0: On average, there is no difference in the way respondents rate the importance of speed and gas mileage.
29
Section 3.2 - How to perform a Paired Samples T-test in SPSS
1. Click Compare Means
2. Click Paired Samples T Test
3. Input Variable 1 (e.g. Car attribute – high gas mileage)
4. Input Variable 2 (e.g. Car attribute – speed)
5. Click OK
32
Section 3.2 - Paired Samples T-test results from SPSS.
• What is the p value?
• Remember, if p is less than 0.05,
we accept HA.
33
Section 3.2 - Paired Samples T-test results from SPSS.
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA.
• P = 0.856
• ∴ We have sufficient evidence to
reject that on average, there is a
difference in the way respondents
rate the importance of speed and gas
mileage.
34
Section 3.3 - Identify which test is appropriate.
• The importance of high gas mileage does not differ between men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean.
• Independent Sample t-test: compares the means for two groups.
• Paired Samples t-test: two sets of observations from the same respondents.
35
Section 3.3 - Identify which test is appropriate.
• The importance of high gas mileage does not differ between men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean.
• Independent Sample t-test: compares the means for two groups.
• Paired Samples t-test: two sets of observations from the same respondents.
36
Section 3.3 - Write down the null hypothesis and the alternative hypothesis.
• The importance of high mileage does not differ between men and women.
Independent Samples t-test
• Alternative Hypothesis
• HA: Mileage µ male ≠ Mileage µ female
• HA: The average likelihood of the importance of high mileage for men is not equal to the
average likelihood of the importance of high mileage for women.
• Null Hypothesis
• H0: Mileage µ male = Mileage µ female
• H0: The average likelihood of the importance of high mileage for men is equal to the average
likelihood of the importance of high mileage for women.
37
Section 3.3 - How to perform an Independent Samples T-test in SPSS
• Click Analyse
• Click Compare Means
• Click Independence-Samples T Test…
• Input test variable (e.g. high gas mileage)
• Input Grouping Variable (e.g. sex of respondent)
• Select Define Groups…
• Select Use specified values
• Group 1: (e.g. 0 = females)
• Group 2: (e.g. 1 = males)
• Click Continue
• Click OK
38
Section 3.3 - Independent Samples T-test in SPSS Results
1. Look at Levene’s Test for Equality of Variances
i. If Levene's test of significance is not
significant, we can assume variances are not
significantly different i.e. “equal variance
assumed”. ∴ , we use the output from the first
row.
39
Section 3.3 - Independent Samples T-test in SPSS Results
1. Look at the level of significance
• If the p-value of the t-test is not significant, it means both, males
and females, are not significantly different.
• P = 0.058
• ∴ There is sufficient evidence to reject that the average likelihood of
the importance of high mileage for men is not equal to the average
likelihood of the importance of high mileage for women.
40
Section 3.4 - Identify which test is appropriate.
• Men do not care about the mileage of the car.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
41
Section 3.4 - Identify which test is appropriate.
• Men do not care about the mileage of the car.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
42
Section 3.4 - Write down the null hypothesis and the alternative hypothesis.
• Men do not care about the mileage of the car.
One sample t-test
• Alternative Hypothesis
• Example 1: HA: µ ≠ 4
• HA: On average, men agree that mileage of the car is either unimportant or important.
• Example 2: HA: µ < 4
• HA: On average, men do not care about the mileage of the car.
• Null Hypothesis
• Example 1: H0: µ = 4
• H0: On average, men do not acknowledge that mileage of the car is unimportant nor important.
• Example 2: H0: µ ≥ 4
• On average, men do care or are indifferent towards the mileage of the car.
43
Section 3.4 – One Sample T-Test in SPSS.
• Remember, we are only concerned with males for this question…
• What do we have to do in SPSS?
44
Section 3 - One Sample t-test data splitting in SPSS
• Click Data
• Click Split File
• Input variable for what groups are based on (e.g. sex of respondent)
• Select Organise output by groups
• Select Sort this file by grouping variables
• Click OK
• Click Analyse
• Click Compare Means
• Click One-Sample T Test…
• Input Test Variable (e.g.Car Attribute – High Gas Mileage)
• Input Test Value (e.g. 4)
• Click OK
45
Section 3 - One Sample t-test results from SPSS
• What is the p-value?
46
Section 3 - One Sample t-test results from SPSS
• P = 0.000
• ∴ There is sufficient evidence
to accept that on average,
men agree that mileage of the
car is either unimportant or
important.
• / on average, men do care
about the mileage of the car.
• Mean = 5.02 (Somewhat
agree)
• ∴ Men do agree that
mileage of the car is
important.
47
Section 3 – Extra Questions on T-Tests
• For the following identify which type of t-test is applicable, write down the alternate and null
hypothesis, and then perform the test in SPSS:
1. Comfort and power are not given the same importance by respondents.
2. The importance of styling does not differ between men and women.
3. Women do care about the speed of the car.
4. People usually seek novelty and are willing to try new innovations in cars (issue 6).
5. Safety is an important attribute in the purchase of a car for both men and women.
48
Section 3 – Extra Questions on T-Tests
1. Comfort and power are not given the same importance by respondents.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
49
Section 3 – Extra Questions on T-Tests
1. Comfort and power are not given the same importance by respondents.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
50
Section 3 – Extra Questions on T-Tests
1. Comfort and power are not given the same importance by respondents.
Paired Samples t-test
• Alternative Hypothesis
• HA : µ comfort - µ power ≠ 0
• [ or µ comfort ≠ µ power ]
• HA: On average, there is a difference in the way respondents rate the importance of comfort and power.
• Null Hypothesis
• H0: µ comfort - µ power = 0
• [ or µ comfort = µ power ]
• H0: On average, there is no difference in the way respondents rate the importance of comfort and power.
51
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = ?
52
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.000
• ∴ We have sufficient evidence to
accept that on average, there is a
difference in the way respondents
rate the importance of comfort and
power.
• Respondents rate the
importance of comfort more
than power
• Comfort = 6.73 (Agree)
• Power = 5.72 (Somewhat
Agree)
53
Section 3 – Extra Questions on T-Tests
1. The importance of styling does not differ between men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
54
Section 3 – Extra Questions on T-Tests
1. The importance of styling does not differ between men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
55
Section 3 – Extra Questions on T-Tests
1. The importance of styling does not differ between men and women.
Independent Samples t-test
• Alternative Hypothesis
• HA: Styling µ male ≠ Styling µ female
• HA: The average importance of styling for men is not equal to the average importance of styling
for women.
• Null Hypothesis
• H0: Styling µ male = Styling µ female
• H0: The average importance of styling for men is equal to the average importance of styling for
women.
56
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = ?
57
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.104
• ∴ We have sufficient evidence to
reject that the average importance of
styling for men is not equal to the
average importance of styling for
women.
58
Section 3 – Extra Questions on T-Tests
1. Women do care about the speed of the car.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
59
Section 3 – Extra Questions on T-Tests
1. Women do care about the speed of the car.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
60
Section 3 – Extra Questions on T-Tests
• Remember, we are only concerned with women for this question…
• What do we have to do in SPSS?
61
Section 3 – Extra Questions on T-Tests
1. Women do care about the speed of the car.
One sample t-test
• Alternative Hypothesis
• Example 1: HA: µ ≠ 4
• HA: On average, women agree that speed of the car is either unimportant or important.
• Example 2: HA: µ > 4
• HA: On average, women do care about the mileage of the car.
• Null Hypothesis
• Example 1: H0: µ = 4
• H0: On average, women do acknowledge that speed of the car is unimportant nor important.
• Example 2: H0: µ ≤ 4
• On average, women do not care or are indifferent towards the speed of the car.
62
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = ?
63
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.000
• ∴ We have sufficient evidence to
accept that on average, women do
care about the mileage of the car / on
average, women agree that speed of
the car is either unimportant or
important.
• Speed = 5.25 (Somewhat agree)
64
Section 3 – Extra Questions on T-Tests
1. People usually seek novelty and are willing to try new innovations in cars (issue 6).
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
65
Section 3 – Extra Questions on T-Tests
1. People usually seek novelty and are willing to try new innovations in cars (issue 6).
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
66
Section 3 – Extra Questions on T-Tests
1. People usually seek novelty and are willing to try new innovations in cars (issue 6).
• One Sample t-test
• Alternative hypothesis
• HA: µ¹4
• On average, people agree or disagree that they usually seek novelty and are willing to try new
innovations in cars.
• Null hypothesis
• H0: µ=4
• On average, people do not agree or disagree that they usually seek novelty and are willing to try new
innovations in cars.
67
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = ?
68
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.156
• ∴ We have sufficient evidence to reject that
on average, people agree or disagree that
they usually seek novelty and are willing to
try new innovations in cars.
69
Section 3 – Extra Questions on T-Tests
1. Safety is an important attribute in the purchase of a car for both men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
70
Section 3 – Extra Questions on T-Tests
1. Safety is an important attribute in the purchase of a car for both men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
71
Section 3 – Extra Questions on T-Tests
1. Safety is an important attribute in the purchase of a car for both men and women.
• Notice that the question does not say equally important! The key to this question is interpretation.
• One sample t-test.
• Alternative hypothesis
• Men
• HA: µ¹4
• Safety is either an important or unimportant attribute in the purchase of a car for men.
• Women
• HA: µ¹4
• Safety is either an important or unimportant attribute in the purchase of a car for women.
• Null hypothesis
• Men
• H0: µ=4
• Safety is neither an important nor unimportant attribute in the purchase of a car for men.
• Women
• H0: µ=4
• Safety is neither an important nor unimportant attribute in the purchase of a car for women.
72
Section 3 – Extra Questions on T-Tests
1. Safety is an important attribute in the purchase of a car for both men and women.
• Notice that the question does not say equally important! The key to this question is interpretation.
• One sample t-test.
• Alternative hypothesis
• Men
• HA: µ>4
• Safety is an important attribute in the purchase of a car for men.
• Women
• HA: µ>4
• Safety is an important attribute in the purchase of a car for women.
• Null hypothesis
• Men
• H0: µ≤4
• Men are either indifferent or do not believe safety is an important attribute in the purchase of a car.
• Women
• H0: µ≤4
• Women are either indifferent or do not believe safety is an important attribute in the purchase of a car.
73
Section 3 – Extra Questions on T-Tests
74
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.000
• ∴ We have sufficient evidence to accept
that on average safety is an important
attribute in the purchase of a car for men.
• Mean = 6.51 (Agree)
75
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.000
• ∴ We have sufficient evidence to accept
that on average safety is an important
attribute in the purchase of a car for
women.
• Mean = 6.86 (Agree)
76
In Two Weeks Time…
• Practicum 2
• Wednesday the 28th of
October, during lecture hours!!
77
Next Week (Flex Week)
• Refresher
• T-Tests
• One-Way ANOVA
78
In Two Weeks Time…
• Mid-Feedback Survey
• Section 3
• ANOVA
Tutorial 4, Week 5
School of Marketing
UNSW Business School
Today…
• Group Allocations
• Section 2
• T-Tests
• Section 3
• T-Tests
• Extra Questions
Section ID Name Group
W11B 5421143 Patrick Chong,Patrick Jet Yao 1
W11B 5418614 Lin Ding,Xiao Lin 1
W11B 5418108 Sabrina Du,Sabrina Laurus 1
W11B 5418518 Teh,Brielle Lee Ann 1
W11B 5421479 Aryan Zaman,Aryan 1
W11B 5361467 Renae Bennett,Renae Paris Mary 2
W11B 5257350 Des Georgaklis,Despina-Marie 2
W11B 5359672 Jayden Simmons,Jayden 2
W11B 5367149 Sandy Sithisakd,Sandy 2
W11B 5401122 Saharsha Tuladhar,Saharsha Dhan 2
W11B 5424607 Deveka Lingam,Deveka 3
W11B 5418278 Mridula Tirumala,Mridula 3
W11B 5421635 Williams,Ava Anais 3
W11B 5509250 Katelyn Wong,Katelyn Xiu En 3
W11B 5422371 Nikki Zhang,Nikki 3
W13B 5420926 Bhatti,Humnah 4
W11B 5416266 Elyssa Chun,Elyssa Grace 4
W11B 5416601 Proteek Huq,Proteek 4
W11B 5417489 Alicia Mu,Alicia Yang Foon 4
W11B 5417429 Britney Truong,Britney 4
W11B 5341949 Camille Chen,Miaoyang ?
W11B 5425140 Yang Chen,Zhouyang Yang ?
Section ID Name Group
W13B 5376290 Haniya Huang,Ya 1
W13B 5438204 Lyric Li,Pengyu 1
W13B 5416542 Vy Nguyen,Vy-Thuc 1
W13B 5422231 Isabella Purnell,Isabella Faith 1
W13B 5354579 Irene Xu,Jingwen 1
W13B 5361655 Elia Berelekhis,Elia 2
W13B 5506861 Chloe Chan,Chloe Jingyi 2
W13B 5348010 Jessica Simon,Jessica 2
W13B 5391681 Rohin Somani,Rohin Munir 2
W13B 5433472 Maggie Yu,Miao 2
W13B 5360619 Deon Hales,Deon Jay Fairley 3
W13B 5359137 Warren Lu,Warren Ding Sheng 3
W13B 5523340 Krishang Nair,Krishang Raakesh 3
W13B 5394819 Kiyah Spinetti,Kiyah Jordan 3
W13B 5366259 Faye Wang,Jianzhe ?
W13B 5402555 Sihan Wang,Sihan ?
4With the:
• Content?
• Tutorials?
• Tests?
• SPSS?
5Group Project Admin
• Excel Sheet Link will be uploaded to our MS Teams File Section / Emailed.
• Tips:
• Create a group chat (MS Teams, Socials).
• Weekly meetings.
• Weekly duties delegated with deadlines.
• Weekly check ups.
6What is a t-test?
• T-test: type of inferential statistics used to determine if there is a significant difference between the
means of two groups.
• A large t-score tells you that the groups are different.
• A small t-score tells you that the groups are similar.
• One variable must be continuous, and the other categorical.
• 3 Types:
• One Sample t-test
• Independent Samples t-test
• Paired Samples t-test
7Types of t-tests
• One Sample t-test: tests the mean of a single group against a known mean.
• Making a conclusion about the population mean compared to a given level.
• We have one question, and one segmentation.
• E.g. a product will be introduced if the population mean performance rating μ is
greater than 7 on a 10-point scale.
• HA: The population mean performance rating is greater than 7 on a 10-point scale.
• H0: The population mean performance rating is less than or equal to 7 on a 10-point
scale.
H0: < 7.0
µ> 7.0
µ
HA:
8Types of t-tests
• Independent Samples t-test: compares the means for two independent groups
• One variable must be continuous, other must be of categorical nature with two independent groups.
• We have one question, and two segmentations.
• E.g. do males and females differ in their likelihood to cook from scratch?
• H0: The average likelihood (i.e. population mean) of men cooking dinner from scratch is
equal to the average likelihood of women cooking dinner from scratch.
• HA: The average likelihood (i.e. population mean) of men cooking dinner from scratch is
not equal to the average likelihood of women cooking dinner from scratch.
µµ 210 : =H
µµ 211 : ¹H
9Independent Samples T-test
• Levene’s Test for Equality of Variances: tests whether the variances of two samples are approximately equal.
• An assumption of t-tests is that variances of the two samples are equal.
• F-test: a procedure used to determine whether there is more variability in the scores of one sample than in the
scores of the other sample.
• Null Hypothesis: the groups we’re comparing all have equal population variances.
• Alternate Hypothesis: the groups we’re comparing do not have equal population variances.
• ROT: If Sig. (P-Value) is greater than 0.05, Levene’s Test is non-significant, so equal variances assumed.
• Interpret T-test using the top row.
• ROT: If Sig. (P-Value) is less than 0.05, Levene’s Test is significant, so equal variances are not assumed.
• Interpret T-test using bottom row.
10
Types of t-tests
• Paired Sample t-test: two sets of observations from the same respondents (e.g. one year).
• Two questions, one segmentation.
• E.g. How much did you like UNSW when you were 18? And, what about now?
• HA: The amount which I liked UNSW when I was 18 is the not equal to the amount which I
like UNSW now.
• H0: The amount which I liked UNSW when I was 18 is the exact same as the amount which
I like UNSW now.
H0: µD = 0
H1: µD ¹ 0
11
13
Section 2.2.3
• Test 3 : Men and women do not differ on their requirements of power from the car.
• Alternative hypothesis
• HA:
• Null hypothesis
• H0:
14
Section 2.2.3
• Test 3 : Men and women do not differ on their requirements of power from the car.
• Independent Samples t-test
• Alternative hypothesis
• HA: µ men power ≠ µ women power
• The average likelihood (i.e. population mean) of power being a requirement from the car for men
is not equal to the average likelihood of power being a requirement from the car for women.
• Null hypothesis
• H0: µ men power = µ women power
• The average likelihood (i.e. population mean) of power being a requirement from the car for men
is equal to the average likelihood of power being a requirement from the car for women.
15
Section 2.2.3 Independent-Samples t-test
• Click Analyse
• Click Compare Means
• Click Independence-Samples T Test…
• Input test variable (e.g. Car attribute – power [power])
• Input Grouping Variable (e.g. sex of respondent)
• Select Define Groups…
• Select Use specified values
• Group 1: (e.g. 0 = females)
• Group 2: (e.g. 1 = males)
• Click Continue
• Click OK
16
Section 2.2.3 Independent-Samples t-test
17
Section 2.2.3 Independent-Samples t-test
1. Look at Levene’s Test for Equality of Variances
i. If Levene’s test of significance is not significant
i.e. greater than 0.05, we can assume variances
are not significantly different i.e. “equal variance
assumed”. Therefore, we use the output from the
first row.
18
Section 2.2.4
• Test 4 : People usually rate the importance of security at a 5.
• Alternative hypothesis
• HA
• Null hypothesis
• H0:
19
Section 2.2.4
• Test 4 : People usually rate the importance of security at a 5.
• One sample t-test
• Alternative hypothesis
• HA: µ>5
• On average, people care about the importance security.
• Null hypothesis
• H0: µ≤5
• On average, people somewhat or do not care, or are indifferent to the importance of
security.
20
One-Sample T-Test in SPSS
• Click Analyse
• Click Compare Means
• Click One-Sample T Test…
• Input Test Variable (e.g. Value – security)
• Input Test Value (e.g. 5)
• Click OK
• https://statistics.laerd.com/spss-tutorials/one-sample-t-test-using-spss-statistics.php
21
Section 2.2.4
• Test 4: People usually rate the importance of security at a 5.
• Remember, if p is less than 0.05, we accept HA.
• P = 0.000
• ∴ We accept that on average, people
care about the importance security.
• Mean = 5.76
22
Section 3 - Questions on T-Tests
• By the time you are done with this page, you should be able to tell when to use the three
different types of t-tests.
• If you are still not clear, please let me know and we’ll go over these again J
23
Section 3 - Questions on T-Tests
• For the following identify which type of t-test is applicable, write down the null and hypotheses,
and then perform the test in SPSS:
1. High gas mileage and speed are not given the same importance by respondents.
2. High gas mileage and speed are given the same importance by respondents.
3. The importance of high mileage does not differ between men and women.
4. Men do not care about the mileage of the car.
24
Section 3.1 - Identify which test is appropriate.
1. High gas mileage and speed are not given the same importance by respondents.
One sample t-test Independent Samples t-test Paired sample t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Sample t-test: two sets of observations from the same respondents (e.g. one year)
25
Section 3.1 - Identify which test is appropriate.
1. High gas mileage and speed are not given the same importance by respondents.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents
26
Section 3.2 - Identify which test is appropriate.
1. High gas mileage and speed are given the same importance by respondents.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents
27
Section 3.2 - Identify which test is appropriate.
1. High gas mileage and speed are given the same importance by respondents.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean.
• Independent Sample t-test: compares the means for two groups.
• Paired Samples t-test: two sets of observations from the same respondents.
28
Section 3.2 - Write down the null hypothesis and the alternate hypothesis.
1. High gas mileage and speed are given the same importance by respondents.
Paired Samples t-test
• Alternate Hypothesis
• HA : µ mileage - µ speed ≠ 0
• [ or µ mileage ≠ µ speed ]
• HA: On average, there is a difference in the way respondents rate the importance of speed and gas mileage.
• Null Hypothesis
• H0: µ mileage - µ speed = 0
• [ or µ mileage = µ speed ]
• H0: On average, there is no difference in the way respondents rate the importance of speed and gas mileage.
29
Section 3.2 - How to perform a Paired Samples T-test in SPSS
1. Click Compare Means
2. Click Paired Samples T Test
3. Input Variable 1 (e.g. Car attribute – high gas mileage)
4. Input Variable 2 (e.g. Car attribute – speed)
5. Click OK
32
Section 3.2 - Paired Samples T-test results from SPSS.
• What is the p value?
• Remember, if p is less than 0.05,
we accept HA.
33
Section 3.2 - Paired Samples T-test results from SPSS.
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA.
• P = 0.856
• ∴ We have sufficient evidence to
reject that on average, there is a
difference in the way respondents
rate the importance of speed and gas
mileage.
34
Section 3.3 - Identify which test is appropriate.
• The importance of high gas mileage does not differ between men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean.
• Independent Sample t-test: compares the means for two groups.
• Paired Samples t-test: two sets of observations from the same respondents.
35
Section 3.3 - Identify which test is appropriate.
• The importance of high gas mileage does not differ between men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean.
• Independent Sample t-test: compares the means for two groups.
• Paired Samples t-test: two sets of observations from the same respondents.
36
Section 3.3 - Write down the null hypothesis and the alternative hypothesis.
• The importance of high mileage does not differ between men and women.
Independent Samples t-test
• Alternative Hypothesis
• HA: Mileage µ male ≠ Mileage µ female
• HA: The average likelihood of the importance of high mileage for men is not equal to the
average likelihood of the importance of high mileage for women.
• Null Hypothesis
• H0: Mileage µ male = Mileage µ female
• H0: The average likelihood of the importance of high mileage for men is equal to the average
likelihood of the importance of high mileage for women.
37
Section 3.3 - How to perform an Independent Samples T-test in SPSS
• Click Analyse
• Click Compare Means
• Click Independence-Samples T Test…
• Input test variable (e.g. high gas mileage)
• Input Grouping Variable (e.g. sex of respondent)
• Select Define Groups…
• Select Use specified values
• Group 1: (e.g. 0 = females)
• Group 2: (e.g. 1 = males)
• Click Continue
• Click OK
38
Section 3.3 - Independent Samples T-test in SPSS Results
1. Look at Levene’s Test for Equality of Variances
i. If Levene's test of significance is not
significant, we can assume variances are not
significantly different i.e. “equal variance
assumed”. ∴ , we use the output from the first
row.
39
Section 3.3 - Independent Samples T-test in SPSS Results
1. Look at the level of significance
• If the p-value of the t-test is not significant, it means both, males
and females, are not significantly different.
• P = 0.058
• ∴ There is sufficient evidence to reject that the average likelihood of
the importance of high mileage for men is not equal to the average
likelihood of the importance of high mileage for women.
40
Section 3.4 - Identify which test is appropriate.
• Men do not care about the mileage of the car.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
41
Section 3.4 - Identify which test is appropriate.
• Men do not care about the mileage of the car.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
42
Section 3.4 - Write down the null hypothesis and the alternative hypothesis.
• Men do not care about the mileage of the car.
One sample t-test
• Alternative Hypothesis
• Example 1: HA: µ ≠ 4
• HA: On average, men agree that mileage of the car is either unimportant or important.
• Example 2: HA: µ < 4
• HA: On average, men do not care about the mileage of the car.
• Null Hypothesis
• Example 1: H0: µ = 4
• H0: On average, men do not acknowledge that mileage of the car is unimportant nor important.
• Example 2: H0: µ ≥ 4
• On average, men do care or are indifferent towards the mileage of the car.
43
Section 3.4 – One Sample T-Test in SPSS.
• Remember, we are only concerned with males for this question…
• What do we have to do in SPSS?
44
Section 3 - One Sample t-test data splitting in SPSS
• Click Data
• Click Split File
• Input variable for what groups are based on (e.g. sex of respondent)
• Select Organise output by groups
• Select Sort this file by grouping variables
• Click OK
• Click Analyse
• Click Compare Means
• Click One-Sample T Test…
• Input Test Variable (e.g.Car Attribute – High Gas Mileage)
• Input Test Value (e.g. 4)
• Click OK
45
Section 3 - One Sample t-test results from SPSS
• What is the p-value?
46
Section 3 - One Sample t-test results from SPSS
• P = 0.000
• ∴ There is sufficient evidence
to accept that on average,
men agree that mileage of the
car is either unimportant or
important.
• / on average, men do care
about the mileage of the car.
• Mean = 5.02 (Somewhat
agree)
• ∴ Men do agree that
mileage of the car is
important.
47
Section 3 – Extra Questions on T-Tests
• For the following identify which type of t-test is applicable, write down the alternate and null
hypothesis, and then perform the test in SPSS:
1. Comfort and power are not given the same importance by respondents.
2. The importance of styling does not differ between men and women.
3. Women do care about the speed of the car.
4. People usually seek novelty and are willing to try new innovations in cars (issue 6).
5. Safety is an important attribute in the purchase of a car for both men and women.
48
Section 3 – Extra Questions on T-Tests
1. Comfort and power are not given the same importance by respondents.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
49
Section 3 – Extra Questions on T-Tests
1. Comfort and power are not given the same importance by respondents.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
50
Section 3 – Extra Questions on T-Tests
1. Comfort and power are not given the same importance by respondents.
Paired Samples t-test
• Alternative Hypothesis
• HA : µ comfort - µ power ≠ 0
• [ or µ comfort ≠ µ power ]
• HA: On average, there is a difference in the way respondents rate the importance of comfort and power.
• Null Hypothesis
• H0: µ comfort - µ power = 0
• [ or µ comfort = µ power ]
• H0: On average, there is no difference in the way respondents rate the importance of comfort and power.
51
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = ?
52
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.000
• ∴ We have sufficient evidence to
accept that on average, there is a
difference in the way respondents
rate the importance of comfort and
power.
• Respondents rate the
importance of comfort more
than power
• Comfort = 6.73 (Agree)
• Power = 5.72 (Somewhat
Agree)
53
Section 3 – Extra Questions on T-Tests
1. The importance of styling does not differ between men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
54
Section 3 – Extra Questions on T-Tests
1. The importance of styling does not differ between men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
55
Section 3 – Extra Questions on T-Tests
1. The importance of styling does not differ between men and women.
Independent Samples t-test
• Alternative Hypothesis
• HA: Styling µ male ≠ Styling µ female
• HA: The average importance of styling for men is not equal to the average importance of styling
for women.
• Null Hypothesis
• H0: Styling µ male = Styling µ female
• H0: The average importance of styling for men is equal to the average importance of styling for
women.
56
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = ?
57
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.104
• ∴ We have sufficient evidence to
reject that the average importance of
styling for men is not equal to the
average importance of styling for
women.
58
Section 3 – Extra Questions on T-Tests
1. Women do care about the speed of the car.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
59
Section 3 – Extra Questions on T-Tests
1. Women do care about the speed of the car.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
60
Section 3 – Extra Questions on T-Tests
• Remember, we are only concerned with women for this question…
• What do we have to do in SPSS?
61
Section 3 – Extra Questions on T-Tests
1. Women do care about the speed of the car.
One sample t-test
• Alternative Hypothesis
• Example 1: HA: µ ≠ 4
• HA: On average, women agree that speed of the car is either unimportant or important.
• Example 2: HA: µ > 4
• HA: On average, women do care about the mileage of the car.
• Null Hypothesis
• Example 1: H0: µ = 4
• H0: On average, women do acknowledge that speed of the car is unimportant nor important.
• Example 2: H0: µ ≤ 4
• On average, women do not care or are indifferent towards the speed of the car.
62
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = ?
63
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.000
• ∴ We have sufficient evidence to
accept that on average, women do
care about the mileage of the car / on
average, women agree that speed of
the car is either unimportant or
important.
• Speed = 5.25 (Somewhat agree)
64
Section 3 – Extra Questions on T-Tests
1. People usually seek novelty and are willing to try new innovations in cars (issue 6).
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
65
Section 3 – Extra Questions on T-Tests
1. People usually seek novelty and are willing to try new innovations in cars (issue 6).
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
66
Section 3 – Extra Questions on T-Tests
1. People usually seek novelty and are willing to try new innovations in cars (issue 6).
• One Sample t-test
• Alternative hypothesis
• HA: µ¹4
• On average, people agree or disagree that they usually seek novelty and are willing to try new
innovations in cars.
• Null hypothesis
• H0: µ=4
• On average, people do not agree or disagree that they usually seek novelty and are willing to try new
innovations in cars.
67
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = ?
68
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.156
• ∴ We have sufficient evidence to reject that
on average, people agree or disagree that
they usually seek novelty and are willing to
try new innovations in cars.
69
Section 3 – Extra Questions on T-Tests
1. Safety is an important attribute in the purchase of a car for both men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
70
Section 3 – Extra Questions on T-Tests
1. Safety is an important attribute in the purchase of a car for both men and women.
One Sample t-test Independent Samples t-test Paired Samples t-test
• One Sample t-test: tests the mean of a single group against a known mean
• Independent Sample t-test: compares the means for two groups
• Paired Samples t-test: two sets of observations from the same respondents (e.g. one year)
71
Section 3 – Extra Questions on T-Tests
1. Safety is an important attribute in the purchase of a car for both men and women.
• Notice that the question does not say equally important! The key to this question is interpretation.
• One sample t-test.
• Alternative hypothesis
• Men
• HA: µ¹4
• Safety is either an important or unimportant attribute in the purchase of a car for men.
• Women
• HA: µ¹4
• Safety is either an important or unimportant attribute in the purchase of a car for women.
• Null hypothesis
• Men
• H0: µ=4
• Safety is neither an important nor unimportant attribute in the purchase of a car for men.
• Women
• H0: µ=4
• Safety is neither an important nor unimportant attribute in the purchase of a car for women.
72
Section 3 – Extra Questions on T-Tests
1. Safety is an important attribute in the purchase of a car for both men and women.
• Notice that the question does not say equally important! The key to this question is interpretation.
• One sample t-test.
• Alternative hypothesis
• Men
• HA: µ>4
• Safety is an important attribute in the purchase of a car for men.
• Women
• HA: µ>4
• Safety is an important attribute in the purchase of a car for women.
• Null hypothesis
• Men
• H0: µ≤4
• Men are either indifferent or do not believe safety is an important attribute in the purchase of a car.
• Women
• H0: µ≤4
• Women are either indifferent or do not believe safety is an important attribute in the purchase of a car.
73
Section 3 – Extra Questions on T-Tests
74
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.000
• ∴ We have sufficient evidence to accept
that on average safety is an important
attribute in the purchase of a car for men.
• Mean = 6.51 (Agree)
75
Section 3 – Extra Questions on T-Tests
• What is the p value?
• Remember, if p is less than 0.05, we accept
HA
• P = 0.000
• ∴ We have sufficient evidence to accept
that on average safety is an important
attribute in the purchase of a car for
women.
• Mean = 6.86 (Agree)
76
In Two Weeks Time…
• Practicum 2
• Wednesday the 28th of
October, during lecture hours!!
77
Next Week (Flex Week)
• Refresher
• T-Tests
• One-Way ANOVA
78
In Two Weeks Time…
• Mid-Feedback Survey
• Section 3
• ANOVA
