A car dealer suspects that the weather of the day of the test drive has an effect on the purchasing behavior. The car dealer recorded the observations in a cross-tab. Sunny Cloudy Rainy Total Purchase Made 6 4 0 Purchase NOT Made 0 4 6 Total Using a Chi-squared test, test the null hypothesis that weather and purchasing behavior are independent against the alternative that they are dependent.
A car dealer suspects that the weather of the day of the test drive has an effect on the purchasing behavior. The car dealer recorded the observations in a cross-tab.
| Sunny | Cloudy | Rainy | Total | |
| Purchase Made | 6 | 4 | 0 | |
| Purchase NOT Made | 0 | 4 | 6 | |
| Total |
Using a Chi-squared test, test the null hypothesis that weather and purchasing behavior are independent against the alternative that they are dependent.
The variables "Purchase Made," "Purchase NOT Made," Sunny", Cloudy," and "Rainy" are best characterized as
Question 1 options:
|
A
|
Categorical data |
|
B
|
Quantitative data |
|
C
|
Continuous data |
|
D
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All of the above |
How many test rides are being examined in this Chi-squared test?
|
A
|
4 |
|
B
|
6 |
|
C
|
10 |
|
D
|
20 |
If car purchase and weather on test drive were independent
|
A
|
1 |
|
B
|
2 |
| C |
3 |
|
D
|
4 |
If car purchase and weather on test drive were independent events, what would be the expected number of the combination of "Purchase NOT Made" and "Cloudy?"
|
A
|
1 |
|
B
|
2 |
|
C
|
3 |
|
D
|
4 |
How many degrees of freedom do you have in this test?
|
A
|
1 |
|
B
|
2 |
|
C
|
3 |
|
D
|
4 |
The chi-squared test statistic is X^2 =
|
A
|
6 |
|
B
|
8 |
|
C
|
10 |
|
D
|
12 |
What's the probability that if in the true population there is no difference between weather and purchasing behavior that your actual observations come from such a population? In other words, the p-value of your test-statistics is
|
A
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less than 1 percent |
|
B
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between 1 and 2.5 percent |
|
C
|
between 2.5 and 5 percent |
|
D
|
More than 5 percent |
Assume that you reject the null hypothesis of no-difference at �=0.05. This means that if you assume that there is no difference in the population between weather and purchasing behavior, and the probability of getting a sample that deviates as much from the
|
A
|
Reject the null |
|
B
|
Fail to reject the null |
|
C
|
Wouldn't know what to do |
| D |
Would flip a coin to decide |
A type I error occurs whenever you
|
A
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Accept the null when it is false |
|
B
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Reject the null when it is true |
|
C
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Reject the alternative when the null is true |
|
D
|
Accept the null when the alternative is false |
The probability that in your statistical decision you made a Type I error is
|
A
|
less than 1 percent |
|
B
|
between 1 and 2.5 percent |
|
C
|
between 2.5 and 5 percent |
| D |
More than 5 percent |
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