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using data set c http:/ tpx Edit View Favorites Tools Help anon | Easy-WebPrint EX. Print & Preview IE, clip E Auto Clip Clip List USAA Military Home, Life .. Home -FAFSA on the We.. & ASU Online Degree Progra... Dollar Shave Club CustomT Answer the following questions. (a) Using Data Set C, ill in the missing data. (Round your p-values to 4 decimal places and other answers to 2 decimal places.) 0.28 ANOVA table Source 12.57 Variables p-value Intercept Floor Offices Entrances Age Freeway 0.00 (b) The predictors whose p-values are less than 0 05 are (You may select more than one answer. Click the box with a check mark for the correct answer and double click to empty the box for the wrong answer.) FreewayExplanation / Answer
(a). We had just copied the data into excel and then save it in ‘.csv’ format. Then we had run the data on STATA 12 version.
Follow the below mentioned steps—
Now, we will get a regression result as below—
. regress assessed floor offices entrances age freeway
Source | SS df MS Number of obs = 32
-------------+------------------------------ F( 5, 26) = 150.38
Model | 6210438.98 5 1242087.8 Prob > F = 0.0000
Residual | 214747.017 26 8259.50064 R-squared = 0.9666
-------------+------------------------------ Adj R-squared = 0.9601
Total | 6425186 31 207264.065 Root MSE = 90.882
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assessed | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
floor | .2506604 .021992 11.40 0.000 .2054552 .2958655
offices | 97.54986 31.04216 3.14 0.004 33.7418 161.3579
entrances | 72.63878 39.04763 1.86 0.074 -7.624758 152.9023
age | -.4732657 1.210279 -0.39 0.699 -2.961029 2.014497
freeway | 116.4807 35.03906 3.32 0.003 44.45685 188.5045
_cons | -57.19445 72.53538 -0.79 0.438 -206.2931 91.90415
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Now, if we look at the p-values, we can easily put down the missing values that we are being asked. These are listed below—
Variables p-values
Floor 0.00
Offices 0.00
Entrances 0.07
Age 0.70
Freeway 0.00
Although our regression analysis is a bit different from what the questioner had done as we can judge from the value of R2, but the process is same.
Our analysis is more relevent as the value of R2 is nearly 1 (the highest value) whereas the value of R2 in the analysis done by the questioner is just 0.28, which is very small in terms of the strength of the test (or, the fitting of the regression line with the actual data).
(b). So, the variables whose p-values are less than 0.05 are: Floor; Offices; Freeway.
Therefore, these variables have significant impact on the assessment as compared to the other variables.
(c). From the above regression table we could see that the variables which are significant are also significant in p-values. This is because we compute the p-values using the t-values only.
Basically the probability of t-values being significant is the p-values that we compute.
So, the correct option is ‘True’.
(d). Although we could use both t-values and p-values are used for determining the significance of a test; but the p-values are more preferred because they calculate the probability at which the null hypothesis could be rejected, which is a stronger point as compared to the t-values which is used to determine after a certain t-value (the critical value) the null hypothesis being rejected or not.
So, the p-values is more stronger in this sense.
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