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1. A researcher has asked you to re-evaluate this data below. Only, this time th

ID: 3363836 • Letter: 1

Question

1. A researcher has asked you to re-evaluate this data below. Only, this time the researcher has asked you to estimate an OLS model regressing the effect of both the effect of teacher experience and time of day on student test scores

Start by writing out, in complete sentences and using the statistical notation, the re- searcher’s hypotheses about the effect of time of day and teacher experience on student test scores.

Next, estimate, a multivariate OLS model that estimates the effect of time of day and teacher experience on student test scores. Report those results in a table that is publication quality.

Case                                        Score                                  Teacher                                   Time

1

98

High Experience

Morning

2

50

High Experience

Morning

3

88

Low Experience

Morning

4

93

Low Experience

Morning

5

75

High Experience

Morning

6

90

Low Experience

Morning

7

93

High Experience

Afternoon

8

100

Low Experience

Afternoon

9

95

High Experience

Afternoon

10

98

High Experience

Afternoon

11

91

Low Experience

Afternoon

12

100

Low Experience

Afternoon

1

98

High Experience

Morning

2

50

High Experience

Morning

3

88

Low Experience

Morning

4

93

Low Experience

Morning

5

75

High Experience

Morning

6

90

Low Experience

Morning

7

93

High Experience

Afternoon

8

100

Low Experience

Afternoon

9

95

High Experience

Afternoon

10

98

High Experience

Afternoon

11

91

Low Experience

Afternoon

12

100

Low Experience

Afternoon

Explanation / Answer

First the data is convert into indicator varibles as

Regression Analysis: Score versus Teacher_High Exp, Time_Afternoon

The regression equation is
Score = 86.8 - 8.83 Teacher_High Experience + 13.8 Time_Afternoon


Predictor Coef SE Coef T P
Constant 86.750 6.205 13.98 0.000
Teacher_High Experience -8.833 7.165 -1.23 0.249
Time_Afternoon 13.833 7.165 1.93 0.086


S = 12.4100 R-Sq = 36.8% R-Sq(adj) = 22.8%


Analysis of Variance

Source DF SS MS F P
Regression 2 808.2 404.1 2.62 0.127
Residual Error 9 1386.1 154.0
Total 11 2194.2


Source DF Seq SS
Teacher_High Experience 1 234.1
Time_Afternoon 1 574.1


Unusual Observations

Teacher_High
Obs Experience Score Fit SE Fit Residual St Resid
2 1.00 50.00 77.92 6.21 -27.92 -2.60R

R denotes an observation with a large standardized residual.

The OLS model of regression equation is
Score = 86.8 - 8.83 Teacher_High Experience + 13.8 Time_Afternoon

Case Score Teacher Time Teacher_High Experience Teacher_Low Experience Time_Afternoon Time_Morning 1 98 High Experience Morning 1 0 0 1 2 50 High Experience Morning 1 0 0 1 3 88 Low Experience Morning 0 1 0 1 4 93 Low Experience Morning 0 1 0 1 5 75 High Experience Morning 1 0 0 1 6 90 Low Experience Morning 0 1 0 1 7 93 High Experience Afternoon 1 0 1 0 8 100 Low Experience Afternoon 0 1 1 0 9 95 High Experience Afternoon 1 0 1 0 10 98 High Experience Afternoon 1 0 1 0 11 91 Low Experience Afternoon 0 1 1 0 12 100 Low Experience Afternoon 0 1 1 0