Question

You want to construct a multiple linear regression model. The dependent variable is Y and independent variables are x1 and x2. The samples and STATA outputs are provided:

How would you make an ANOVA from the following information?

Y | X1 | X2 |

3 | 2 | 1 |

4 | 1 | 2 |

6 | 3 | 3 |

6 | 3 | 4 |

7 | 4 |
5 |

STATA

Y |
Coef. |
Std. Err. |
t |
P> abs. value (t) |
95% confidence interval |

X1 | 0.25 | 0.4677072 | 0.53 | 0.646 | -1.762382 , 2.262382 |

X2 | 0.85 | 0.3372684 | 2.52 | 0.128 | -.601149 , 2.301149 |

_cons | 2 | 0.7245688 | 2.76 | 0.110 | -1.117568 , 5.117568 |

Answer #1

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You want to construct a multiple linear regression model. The
dependent variable is Y and independent variables are x1 and x2.
The samples and STATA outputs are provided:
Y
X1
X2
3
2
1
4
1
2
6
3
3
6
3
4
7
4
5
STATA
Y
Coef.
Std. Err.
t
P> abs. value (t)
95% confidence interval
X1
0.25
0.4677072
0.53
0.646
-1.762382 , 2.262382
X2
0.85
0.3372684
2.52
0.128
-.601149 , 2.301149
_cons
2
0.7245688
2.76
0.110...

You want to construct a multiple linear regression model. The
dependent variable is Y and independent variables are x1 and x2.
The samples and STATA outputs are provided:
Y
X1
X2
3
2
1
4
1
2
6
3
3
6
3
4
7
4
5
STATA
Y
Coef.
Std. Err.
t
P> abs. value (t)
95% confidence interval
X1
0.25
0.4677072
0.53
0.646
-1.762382 , 2.262382
X2
0.85
0.3372684
2.52
0.128
-.601149 , 2.301149
_cons
2
0.7245688
2.76
0.110...

(By Hand) For the dependent variable Y and the independent
variables X1 and X2, the linear regression model is given by:
Y=0.08059*X1-0.16109*X2+5.26570. Complete the following table:
Actual Y
X1
X2
Predicted Y
Prediction Error
6
6.8
4.7
3.1
5.3
5.5
5.8
4.5
6.2
4.5
8.8
7
4.5
6.8
6.1
3.7
8.5
5.1
5.4
8.9
4.8
5.1
6.9
5.4
5.8
9.3
5.9
5.7
8.4
5.4

You are given the following partial Stata output:
regress y x z
Source | SS df MS
-------------+---------------------------------------
Model |
810
1
Residual | 270
19
-------------+----------------------------------------
Total |
1080
20
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95%
Conf. Interval]
------------------------------------------------------------------------------
x| 9 1.5
z| 6 3.0
Number of obs = 21
F( 2, 18) =
Prob > F =
R-squared =
Adj R-squared =
Root MSE =
Fill out all the remaining entries in this Stata output....

The following multiple regression model uses wage,
which is hourly earnings in dollars, as dependent variable, IQ as
in IQ test scores as independent variables to run a regression as
follows. STATA commands and outputs are given on the STATA output
page. Answer the following questions. (23 points)
wage= β0 +β1 IQ + u
According to the STATA output, what are the minimum and the
maximum for education years (denoted as educ) in the
sample? (4 points)
Write down the...

Consider the following data for a dependent variable y and two
independent variables, x1 and x2 . x 1 x 2 y 29 13 94 46 11 109 25
17 112 50 16 178 40 6 95 51 19 175 74 7 170 36 13 117 59 13 143 76
17 212 Round your all answers to two decimal places. Enter negative
values as negative numbers, if necessary. a. Develop an estimated
regression equation relating y to x1 . ŷ...

Multiple linear regression results:
Dependent Variable: Cost
Independent Variable(s): Summated Rating
Cost = -43.111788 + 1.468875 Summated Rating
Parameter estimates:
Parameter
Estimate
Std. Err.
Alternative
DF
T-Stat
P-value
Intercept
-43.111788
10.56402
≠ 0
98
-4.0810021
<0.0001
Summated Rating
1.468875
0.17012937
≠ 0
98
8.633871
<0.0001
Analysis of variance table for multiple regression model:
Source
DF
SS
MS
F-stat
P-value
Model
1
8126.7714
8126.7714
74.543729
<0.0001
Error
98
10683.979
109.02019
Total
99
18810.75
Summary of fit:
Root MSE: 10.441273
R-squared: 0.432...

The following information regarding a dependent variable (Y) and
an independent variable (X) is provided.
Y
X
4
2
3
1
4
4
6
3
8
5
SSE = 6
SST = 16
Refer to Exhibit 12-4. The least squares estimate of the slope
is
Question 7 options:
1
2
3
4

You are given the following information about y and x. Dependent
Variable y Independent Variable x 5 1 4 2 3 3 2 6 1 8 Refer to
Exhibit 3. The least squares estimate of b1 (slope) equals

You are given the following information about y and x.
Dependent Variable y
Independent Variable x
12
4
3
6
7
2
6
4
Refer to Exhibit 12-6. The least squares estimate of
b0 equals
Question 14 options:
1
-1
-11
11

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