Linear Regression ID Verbal ✓ Model Builder Predictors Age Gender Dependent Variable Math Covariates Age Factors Gender Blocks Block 1 Gender Block 2 Age + Add New Block X X Model 1 2 Model Comparisons Comparison Model 1 R 0.0433 0.2275 Model -2 Omnibus ANOVA Test R² 0.00187 0.05178 Model Specific Results Model 2 Intercept Gender: Age AR² 0.0499 Gender Age Residuals Note. Type 3 sum of squares Sum of Squares Female - Male Model Coefficients - Math Predictor 365 3328 63242 F 0.0338 0.4641 -8.63 7.25 0.895 df 1 1 F 17 SE Estimate 399.95 146.17 27.54 7.67 Overall Model Test df1 df2 1 2 df1 1 Mean Square 365 3328 3720 t 18 17 -0.313 0.946 df2 17 F 0.0981 0.8947 P 2.736 0.014 0.758 0.357 Р 0.856 0.636 Р 0.357 Р 0.758 0.357 [3] Stand. Estimate -0.146 0.226
Linear Regression ID Verbal ✓ Model Builder Predictors Age Gender Dependent Variable Math Covariates Age Factors Gender Blocks Block 1 Gender Block 2 Age + Add New Block X X Model 1 2 Model Comparisons Comparison Model 1 R 0.0433 0.2275 Model -2 Omnibus ANOVA Test R² 0.00187 0.05178 Model Specific Results Model 2 Intercept Gender: Age AR² 0.0499 Gender Age Residuals Note. Type 3 sum of squares Sum of Squares Female - Male Model Coefficients - Math Predictor 365 3328 63242 F 0.0338 0.4641 -8.63 7.25 0.895 df 1 1 F 17 SE Estimate 399.95 146.17 27.54 7.67 Overall Model Test df1 df2 1 2 df1 1 Mean Square 365 3328 3720 t 18 17 -0.313 0.946 df2 17 F 0.0981 0.8947 P 2.736 0.014 0.758 0.357 Р 0.856 0.636 Р 0.357 Р 0.758 0.357 [3] Stand. Estimate -0.146 0.226
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
How does the association between age and math score change if gender is included in the analysis?
![Data Analyses
daļ
ANOVA Regression
Exploration T-Tests
Linear Regression
ID
Verbal
✓ Model Builder
Predictors
Age
Gender
↑
88
Frequencies
Dependent Variable
Math
Covariates
Age
Factors
Gender
Blocks
Block 1
Factor
Gender
Block 2
Age
+ Add New Block
X
Model
1
2
jamovi - SAT FroDis
Model Comparisons
Comparison
Model
1
R
0.0433
0.2275
Model
2
Intercept
Gender:
Omnibus ANOVA Test
R²
0.00187
0.05178
Age
Model Specific Results Model 2
AR²
0.0499
365
Gender
Age
3328
Residuals
63242
Note. Type 3 sum of squares
Model Coefficients - Math
Predictor
Female - Male
Sum of Squares
Estimate
399.95
F
-8.63
7.25
0.0338
0.4641
F
0.895
df
1
1
17
SE
146.17
27.54
7.67
Overall Model Test
df2
df1
1
2
df1
1
Mean Square
365
3328
3720
t
2.736
-0.313
0.946
18
17
df2
17
F
0.0981
0.8947
Р
0.014
0.758
0.357
Р
0.856
0.636
Р
0.357
Р
0.758
0.357
[3]
Stand. Estimate
-0.146
0.226](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3a320799-95d9-4e30-88e6-566cae227b49%2F833d58a5-ed68-49a0-a96c-3fcd604a3515%2Fkbo7qcp_processed.png&w=3840&q=75)
Transcribed Image Text:Data Analyses
daļ
ANOVA Regression
Exploration T-Tests
Linear Regression
ID
Verbal
✓ Model Builder
Predictors
Age
Gender
↑
88
Frequencies
Dependent Variable
Math
Covariates
Age
Factors
Gender
Blocks
Block 1
Factor
Gender
Block 2
Age
+ Add New Block
X
Model
1
2
jamovi - SAT FroDis
Model Comparisons
Comparison
Model
1
R
0.0433
0.2275
Model
2
Intercept
Gender:
Omnibus ANOVA Test
R²
0.00187
0.05178
Age
Model Specific Results Model 2
AR²
0.0499
365
Gender
Age
3328
Residuals
63242
Note. Type 3 sum of squares
Model Coefficients - Math
Predictor
Female - Male
Sum of Squares
Estimate
399.95
F
-8.63
7.25
0.0338
0.4641
F
0.895
df
1
1
17
SE
146.17
27.54
7.67
Overall Model Test
df2
df1
1
2
df1
1
Mean Square
365
3328
3720
t
2.736
-0.313
0.946
18
17
df2
17
F
0.0981
0.8947
Р
0.014
0.758
0.357
Р
0.856
0.636
Р
0.357
Р
0.758
0.357
[3]
Stand. Estimate
-0.146
0.226
![Data
HO
Exploration T-Tests
> Model Builder
Analyses
Linear Regression
Fit Measures
R
R²
Adjusted R²
юн
88
ANOVA Regression Frequencies
> Reference Levels
> Assumption Checks
Model Fit
HOH
юч
Factors
Factor
Overall Model Test
F test
→→
jamovi - SAT FroDis
Linear Regression
Model Fit Measures
1
Model
R
R²
0.215 0.0463
Omnibus ANOVA Test
Sum of Squares
Age
3088
Residuals
63607
Note. Type 3 sum of squares
Model Coefficients - Math
Predictor Estimate
Intercept 402.04
Age
6.92
SE
142.31
df
F
0.874
1
18
t
Overall Model Test
df1
df2
1
Mean Square
3088
3534
2.825
7.40 0.935
Р
0.011
0.362
18
F
0.874
P
0.362
0.215
P
0.362
Stand. Estimate
[3]](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3a320799-95d9-4e30-88e6-566cae227b49%2F833d58a5-ed68-49a0-a96c-3fcd604a3515%2F41rtwu6_processed.png&w=3840&q=75)
Transcribed Image Text:Data
HO
Exploration T-Tests
> Model Builder
Analyses
Linear Regression
Fit Measures
R
R²
Adjusted R²
юн
88
ANOVA Regression Frequencies
> Reference Levels
> Assumption Checks
Model Fit
HOH
юч
Factors
Factor
Overall Model Test
F test
→→
jamovi - SAT FroDis
Linear Regression
Model Fit Measures
1
Model
R
R²
0.215 0.0463
Omnibus ANOVA Test
Sum of Squares
Age
3088
Residuals
63607
Note. Type 3 sum of squares
Model Coefficients - Math
Predictor Estimate
Intercept 402.04
Age
6.92
SE
142.31
df
F
0.874
1
18
t
Overall Model Test
df1
df2
1
Mean Square
3088
3534
2.825
7.40 0.935
Р
0.011
0.362
18
F
0.874
P
0.362
0.215
P
0.362
Stand. Estimate
[3]
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