I have inputed all data into an SPSS system I need help answering 3-5. I have attatched the data graphs from SPSS.

Questions

research question for the problem described above. Identify and label the independent and dependent variables.

Research Question:

- Does the years of higher experience of an employee indicate a higher annual salary compared to less years of experience.

IV: Experience

DV: Annual Salary

2. Use the data provided to produce data input and output files to answer the research question.

3. Determine and explain the values on your output for (R), (R²), (adjusted R²), and (shrinkage).

4. Should you reject any of your bivariate null hypotheses or your model null hypothesis based upon the significance levels? Write the statements of these null hypotheses and explain the effect size and bivariate correlation between the predictors.

5. Determine and explain whether any of the predictors add significantly to the regression prediction model? Calculate an example regression predicted score.

Regression

Notes
Output Created	20-APR-2023 12:45:10
Comments
Input	Active Dataset	DataSet1
Filter
Weight
Split File
N of Rows in Working Data File	21
Missing Value Handling	Definition of Missing	User-defined missing values are treated as missing.
Cases Used	Statistics are based on cases with no missing values for any variable used.
Syntax	REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT AnnualIncome /METHOD=ENTER Experienceyears Hoursweek.
Resources	Processor Time	00:00:00.03
Elapsed Time	00:00:00.00
Memory Required	2896 bytes
Additional Memory Required for Residual Plots	0 bytes

Descriptive Statistics
	Mean	Std. Deviation	N
AnnualIncome	52900.0000	14875.89007	20
Experienceyears	9.1500	5.40248	20
Hoursweek	42.7000	2.88554	20

Correlations
	AnnualIncome	Experienceyears	Hoursweek
Pearson Correlation	AnnualIncome	1.000	.969	.576
Experienceyears	.969	1.000	.496
Hoursweek	.576	.496	1.000
Sig. (1-tailed)	AnnualIncome	.	<.001	.004
Experienceyears	.000	.	.013
Hoursweek	.004	.013	.
N	AnnualIncome	20	20	20
Experienceyears	20	20	20
Hoursweek	20	20	20

Variables Entered/Removeda
Model	Variables Entered	Variables Removed	Method
1	Hoursweek, Experienceyearsb	.	Enter
a. Dependent Variable: AnnualIncome
b. All requested variables entered.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.976a	.952	.946	3455.83067
a. Predictors: (Constant), Hoursweek, Experienceyears

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	4001522984.965	2	2000761492.482	167.529	<.001b
Residual	203027015.035	17	11942765.590
Total	4204550000.000	19
a. Dependent Variable: AnnualIncome
b. Predictors: (Constant), Hoursweek, Experienceyears

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	Collinearity Statistics
B	Std. Error	Beta	Tolerance	VIF
1	(Constant)	2385.808	12837.813		.186	.855
Experienceyears	2497.779	169.001	.907	14.780	<.001	.754	1.326
Hoursweek	647.764	316.415	.126	2.047	.056	.754	1.326
a. Dependent Variable: AnnualIncome

Collinearity Diagnosticsa
Model	Dimension	Eigenvalue	Condition Index	Variance Proportions
(Constant)	Experienceyears	Hoursweek
1	1	2.832	1.000	.00	.02	.00
2	.167	4.123	.00	.77	.00
3	.002	40.596	1.00	.21	1.00
a. Dependent Variable: AnnualIncome