Descriptive Analysis: http://calcnet.mth.cmich.edu/org/spss/Prj_New_DrugData.htm

Data Set

Procedural Steps

[Analyze] [Descriptive Statistics] [Frequencies]

Transfer over variables of interest

[Statistics] [Mean]; [S.E. (Standard Error)]; or whatever descriptive stats you need [Continue]

[Charts] [None]; [Bar Charts]; [Pie Charts]; [Histogram] [OK]

Output

Statistics

Age GenderBefore_exp

_BPAfter_exp_

BPN Valid 50 50 50 50

Missing 0 0 0 0Mean 61.48 98.3020 88.5980Std. Error of Mean

.920 .73082 .64507

Compute New Variables http://calcnet.mth.cmich.edu/org/spss/Prj_New_DrugData.htm

Data Set

Procedural Steps

[Transform] [Compute Variable]

Type Name of New Variable in “Target Variable”

Transfer First Variable, Click on function, Transfer Second Variable

Optional: [Type&Label] Enter in label for new variable for future reference

[OK]

New Data Set

Independent T-Test (2 Tail) http://calcnet.mth.cmich.edu/org/spss/Prj_New_DrugData.htm

Data Set

Procedural Steps

[Analyze] [Compare Means] [Independent-Samples T-Test]

Transfer Grouping Variable (Variable used to create 2 groups) [Define Groups] (Enter labels for groups), Transfer Test Variables (Can be more than one if doing multiple T-tests

Optional: [Options] change confidence interval

[OK]

Output

Group Statistics

Treatment N Mean Std. Deviation Std. Error Mean

After-Before Control 22 -4.9455 2.25044 .47980

Newdrug 28 -13.4429 5.73091 1.08304

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

F Sig. t df

Sig. (2-

tailed)

Mean

Difference

Std. Error

Difference

95% Confidence

Interval of the

Difference

Lower Upper

After-

Before

Equal variances assumed 11.357 .001 6.557 48 .000 8.49740 1.29591 5.89180 11.10301

Equal variances not assumed 7.173 36.815 .000 8.49740 1.18456 6.09685 10.89795

Linear Regression-Statistics & Plots http://calcnet.mth.cmich.edu/org/spss/Prj_body_fat_data.htm

Data Set

Procedural Steps

[Analyze] [Regression] [Linear]

Transfer Dependent Variable to “Dependent”, Transfer Independent Variable(s) to “Independent(s)”

[Statistics] [Collinearity Diagnostics] [Continue]

[Plots] [Histogram] & [Normal Probability Plot] & Transfer *ZRESID to “Y”, Transfer *ZPRED to “X” [Continue]

[Continue]

Output

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 478.830 3 159.610 4263.256 .000b

Residual .599 16 .037

Total 479.429 19

a. Dependent Variable: Amount of body fat

b. Predictors: (Constant), Midarm circumference, Thigh circumference, Triceps skinfold thickness

* Make sure the scatterplot has no visible trends*

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.

Collinearity Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) -32.327 .713 -45.348 .000

Triceps skinfold thickness .833 .018 .868 46.833 .000 .227 4.400

Thigh circumference .524 .012 .380 42.459 .000 .973 1.028

Midarm circumference .026 .018 .027 1.437 .170 .224 4.459

a. Dependent Variable: Amount of body fat

Linear Regression-Stepwise http://calcnet.mth.cmich.edu/org/spss/Prj_body_fat_data.htm

Data Set

Procedural Steps

[Analyze] [Regression] [Linear] Change “Enter” to “Stepwise”

Transfer Dependent Variable to “Dependent”, Transfer Independent Variable(s) to “Independent(s)”

[Statistics] [Collinearity Diagnostics] [Continue]

[Save] [Standardized] in Residuals column [Continue]

[Continue]

Outputs

Correlation http://calcnet.mth.cmich.edu/org/spss/Prj_body_fat_data.htm

Data Set

Procedural Steps

[Analyze] [Correlation] [Bivariate]

Transfer all variables of interest to “Variables” (Correlations will be found between all variables listed)

Optional: [Options] [Means and Standard Deviations]

Optional: Click on “Correlation Coefficients” to change or add statistical values and “Test of Significance” to change to one-tail or two-tail

[Continue]

Outputs:

Descriptive Statistics

Mean Std. Deviation N

Amount of body fat 25.305 5.0233 20

Triceps skinfold thickness 51.170 5.2346 20

Thigh circumference 27.620 3.6471 20

Midarm circumference 20.195 5.1062 20

Correlations

Amount of

body fat

Triceps

skinfold

thickness

Thigh

circumference

Midarm

circumference

Amount of body fat Pearson Correlation 1 .924** .458* .843**

Sig. (2-tailed) .000 .042 .000

N 20 20 20 20

Triceps skinfold thickness Pearson Correlation .924** 1 .085 .878**

Sig. (2-tailed) .000 .723 .000

N 20 20 20 20

Thigh circumference Pearson Correlation .458* .085 1 .142

Sig. (2-tailed) .042 .723 .549

N 20 20 20 20

Midarm circumference Pearson Correlation .843** .878** .142 1

Sig. (2-tailed) .000 .000 .549

N 20 20 20 20

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

One Way ANOVA Data used from BIOL 354 Fall 2014

Data Set

Procedural Steps

[Analyze] [Compare Means] [One-Way ANOVA]

Transfer Independent Variable into “Factor:” and the Dependent Variable into “Dependent List:”

[Post Hoc] [Tukey’s]

[Continue]

Output

Measurement

Tukey HSDa

SubjCondtion N

Subset for alpha = 0.05

1 2

Female5 8 .01146

FemaleZero 8 .03867

Female15 8 .33711

Female50 8 .36717

MaleZero 8 .84473

Sig. .174 1.000

Means for groups in homogeneous subsets are displayed.

a. Uses Harmonic Mean Sample Size = 8.000.

ANOVA

Measurement

Sum of Squares df Mean Square F Sig.

Between Groups 3.618 4 .904 9.321 .000

Within Groups 3.396 35 .097

Total 7.014 39

Two Way ANOVA http://calcnet.mth.cmich.edu/org/spss/Prj_Super_MarketData.htm

Data Set

Procedural Steps

[Analyze] [General Linear Model] [Univariate]

Transfer the Independent Variables to “Fixed Factors:” and the Dependent Variable to “Dependent Variable:”

[Plots] Enter one variable into “Horizontal Axis” (this will be on the x-axis) and one variable into “Separate Lines”

If an independent variable has more than 2 levels: [Post Hoc] Transfer Variables with 3 or more levels to “Post Hoc Tests for” [Tukey’s]

[Continue]

Output

Tests of Between-Subjects Effects

Dependent Variable: Sales

Source

Type III Sum of

Squares df Mean Square F Sig.

Corrected Model 503.754a 5 100.751 5.397 .007

Intercept 6740.928 1 6740.928 361.121 .000

Shelf 457.568 2 228.784 12.256 .001

Store 7.014 1 7.014 .376 .550

Shelf * Store 30.114 2 15.057 .807 .467

Error 242.667 13 18.667

Total 7342.000 19

Corrected Total 746.421 18

a. R Squared = .675 (Adjusted R Squared = .550)

Sales

Tukey HSDa,b,c

Shelf N

Subset

1 2

1 7 13.57

2 6 17.67

3 6 25.50

Sig. .249 1.000

Chi-Squared Test; Sample Data made by Alexis Tarter from http://www.ling.upenn.edu/~clight/chisquared.htm

Data Set

Procedural Steps

[Analyze] [Descriptive Statistics] Crosstabs

Transfer one variable into “Row(s):” and the other variable/s into “Column(s):”

[Statistics] [Chi-Square] [Continue]

If you want percentages for the table: [Cells] [Row], [Columns], and [Total] [Continue]

[OK]