# Presentation on Statistics procedures

Remake the presentationHi there,I hope you are doing great.Let’s do the last presentation includes all the Statistics procedures ( see the file whic named Statistical procedure), so I want to analyze the same data that we worked on throughout this period. Please make sure to do everything clearly, IT WILL BE FINAL PRESENTATION.Make a story based on the topic as we did before but remake it with a different way.Use the graphs to support data.I provided you with files, please see them.Thank you

Creating a Scatterplot with the Regression Line in SPSSClick GraphsClick Legacy DialogsClick ScatterdotY Dependent Variable (1st box)X Independent Variable (2nd box)Double Click the scatterplotSelect the Regression Icon

Running the Regression in SPSSClick AnalyzeClick RegressionPlug the appropriate variable in the Dependent and Independentvariables boxClick OkModel Summary

Model
R
R Square
Std. Error of theEstimate

1
.580a
.337(coefficient of determination)
.334
5.4691

a. Predictors: (Constant), age
ANOVAb

Model
Sum of Squares
df
Mean Square
F
Sig.

1
Regression
3447.327(explainedvariation)
1
3447.327
115.251(F calculatedvalue)
.000a

Residual
6789.911(unexplainedvariation)
227
29.912
(F significance)

Total
10237.238(total variation)
228

a. Predictors: (Constant), ageb. Dependent Variable: years since separation

Coefficientsa

Model
Unstandardized Coefficients
StandardizedCoefficients
t
Sig.

B
Std. Error
Beta

1
(Constant)
-8.289 (a orintercept)
1.578
-5.254
.000 (tsignificance)

age
.394 (b orslope)
.037
.580
10.735(tcalculatedvalue)
.000

a. Dependent Variable: years since separation
Interpretation (given the above example)Coefficient of determination: 33.7% of the variation in years since separation canbe explained by age.Given the F significance of .000 (<.05), the regression model is a good fit.Given the intercept = -8.289: People in the data set will be separated less than8.289 years if they were 0 or if we do not know their age.Given the slope = .394: For every year increase in age there is an increase by.394 in years since separation.Given the T significance of .000 (<.05), the independent variable (age) is a goodpredictor of the dependent variable (number of years since separation).

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