Download Testing Hypotheses with SPSS and more Study notes Statistics in PDF only on Docsity!
Is the mean hourly rate of male workers $12.00?
Dataset: CPSPUB-FEB
T-Test
One-Sample Statistics
Hourly pay rate 2997 12.0522 6.6282.
N Mean Std. Deviation
Std. Error Mean
One-Sample Test
Hourly pay rate .431 2996 .666 5.224E-02 -.1852.
t df Sig. (2-tailed)
Mean Difference Lower Upper
95% Confidence Interval of the Difference
Test Value = 12
The sample mean is 12.0522. We're comparing that against the claimed mean of $12.00.
With a two-tailed probability value of 0.666, we see that the sample mean is not
significantly different from the claimed mean.
Do men make more per hour than women?
Dataset: CPSPUB-FEB
T-Test
Group Statistics
3432 10.0534 5.3606 9.150E-
Sex Male Female
Hourly pay rate
N Mean Std. Deviation
Std. Error Mean
Independent Samples Test
112.944 .000 13.359 6427 .000 1.9989 .1496 1.7055 2. 13.171 5756.093 .000 1.9989 .1518 1.7014 2.
Equal variances assumed Equal variances not assumed
Hourly pay rate
F Sig.
Levene's Test for Equality of Variances
t df Sig. (2-tailed)
Mean Difference
Std. Error Difference Lower Upper
95% Confidence Interval of the Difference
t-test for Equality of Means
Levene's Test for Equality of Variances has a significance of 0.000. This means that the variances
are not equal and you should read from the "equal variances not assumed" row.
In this case, it doesn't really matter, because the two-tailed p-value is 0.000 in both cases. However,
please note that this is a one-tail (right-tail) test, and so the p-value given by SPSS must be divided
by 2 to get the one-tail p-value. However, 0.000 / 2 is still 0.000. So, there is a definite difference in
hourly rates.
SPSS doesn't tell you directly which one is larger, but since you know there is a difference, look at
the "group statistics" and see that the mean for men is 12.0522 and the mean for women is 10.0534.
This lets us know that men make more per hour than women.
Is there any difference in the exam scores for Math 113?
Oneway
Dataset: SP2000GRADES
Descriptives Exam score
Total
N Mean Std. Deviation Std. Error Lower Bound Upper Bound
95% Confidence Interval for Mean Minimum Maximum
Test of Homogeneity of Variances Exam score
Levene Statistic df1 df2 Sig.
ANOVA
Exam score
Between Groups Within Groups Total
Sum of Squares df Mean Square F Sig.
The "Descriptives" gives the mean for each exam and also the grand mean. The test for
homogeneity of variances has a p-value 0.244, which is not significant enough to say the variances
are different, so we're assuming equal variances.
The "ANOVA" table has a p-value of 0.169, which is not significant. So, there is no significant
difference in the means.
Don't do any Post Hoc tests to find the differences in the means because there aren't any.
Do at least 2/3 of the people work 40 hours or more per week?
NPar Tests
Dataset: CPSPUB-FEB
Binomial Test
<= 39 2263 .352 .333 .001a
39 4166. 6429 1.
Group 1 Group 2 Total
Number of hours usually worked
Category N
Observed Prop. Test Prop.
Asymp. Sig. (1-tailed)
a.Based on Z Approximation.
Notice that we get a one-tailed p-value from SPSS. If you wanted to know whether it was exactly 2/3,
then you would have to double the p-value for a two-tail test. However, also notice that it is 0.001,
which is statistically significant. Therefore we can reject our claim that at least 2/3 of the people work
40 hours or more per week. They don't. It's closer to 64.8% than 2/3.
Are 78% White, 16% Black, 2% Indian, and 4% Asian?
Dataset: CPSPUB-FEB
NPar Tests
Chi-Square Test
Frequencies
Race
White Black American Indian, Aleut, Eskimo Asian or Pacific Islander Total
Observed N Expected N Residual
Test Statistics
Chi-Squarea df Asymp. Sig.
Race
a.0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 128.6.
The p-value is 0.000, so the observed frequencies do not agree with the expected frequencies. The
goodness of fit test doesn't tell us where the differences lie, only that there was at least one that was
different.
