SPSS LAB FILE 1

Transcription

1 SPSS LAB FILE 1

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4 OBJECTIVE 1: Transporation of Data Set to SPSS Editor INPUTS: Files: group1.xlsx, group1.txt PROCEDURE FOLLOWED: 1. Through excel COMMANDS: 1. File 2. Open 3. Data OUTPUT: File: group1.sav 4

5 2. Through Text file COMMANDS: 1. File 2. Read text data OUTPUT: 5

6 CONCLUSION: Any document file in excel,text etc. formats can be transported to SPSS editor window. PRECAUTIONS: 1. There should be proper spacing between different variables in text file. 2. Extensions of the files should be strictly taken care of. 6

7 OBJECTIVE 2: Splitting and Merging of files 1. Merging of files: a. By cases b. By variables INPUTS: Files: 1.sav,2.sav,3.sav PROCEDURE FOLLOWED: a. By cases Merging of 1.sav & 2.sav : File: 1.sav File: 2.sav 7

8 COMMANDS: 1. Data 2. Merge Files 3. Add cases MERGED FILE: 8

9 b. By variables Merging of 1.sav & 3.sav : File: 3.sav 9

10 COMMANDS: 1. Data 2. Merge Files 3. Add variables MERGED FILE: 10

11 2. 1-way merging: Both files provide cases Merging of 1.sav & 4.sav File: 4.sav COMMANDS: 1. Data 11

12 2. Merge Files 3. Add variables OUTPUT: MERGED FILE: 3. 2-way merging: Non-active dataset is keyed table Merging of 1.sav & 4.sav COMMANDS: 12

13 1. Data 2. Merge Files 3. Add variables MERGED FILE: Active dataset is keyed table COMMANDS: 1. Data 13

14 2. Merge Files 3. Add variables MERGED FILE: 14

15 4. Split file according to a variable and filtering. INPUTS: Files: group1.sav PROCEDURE FOLLOWED: COMMANDS: 1. Data 2. Split File OUTPUT: 15

16 COMMANDS: 1. Data 2. Select Cases (where 12 th class marks> 90) OUTPUT: 16

17 CONCLUSION: 1. Different styles of merging can be applied easily to files according to our requirements. 2. Small extracts of very large document files can be viewed easily through splitting and filtering of data on given specific cases. PRECAUTIONS: 1. The format of the files/variables to be merged should be same. 2. The conditional statements on the basis of which the file is to be splited should be given carefully. 17

18 OBJECTIVE 3: Missing Values INPUTS: File: group1.sav PROCEDURE FOLLOWED: COMMANDS: 1. Transform 2. Record into Same Variables OUTPUT: 18

19 CONCLUSION: Missing Values of the variable MCEPreferenceBefore are replaced with 0. PRECAUTIONS: 1. Missing values should be very carefully taken care of during calculations or graphical plotting. 2. Strings expressions cannot be given in place of missing values. 19

20 OBJECTIVE 4: Pictographical reperesentation of data. INPUTS: File: group1.sav PROCEDURE FOLLOWED: COMMANDS: 1. Graphs 2. Chart builder Line graph OUTPUT: Pie chart OUTPUT: 20

21 Histogram graph OUTPUT: CONCLUSION: We conclude that for any given data various types of graphs can be represented easily. PRECAUTIONS: 1. Variables should be choosen carefully during plotting graphs. 2. Graph labels should be chosen appropriately. 21

22 OBJECTIVE 5: To compute variables. INPUTS: File: group1.sav PROCEDURE FOLLOWED: COMMANDS: 1. Transform 2. Compute Variable 22

23 OUTPUT: Calculating the sum of AQ1, AQ2 & AQ3: 23

24 OUTPUT: CONCLUSION: Very tedious calculations can be done very easily. PRECAUTIONS: 1. Variable names should be taken carefully. 2. We should take care that variable do not overlap. 24

25 OBJECTIVE 6: Distribution curves INPUTS: File: group1.sav PROCEDURE FOLLOWED: COMMANDS: 1. Graphs 2. Legacy dialogs 3. Histogram Row wise: Finding frequency by gender to weight OUTPUT: 25

26 Column wise: Finding frequency of gender to 12th class marks OUTPUT: 26

27 Both row wise and column wise: finding frequency of gender with aieee marks at row and 12th class marks at column OUTPUT: CONCLUSION: Concising the data in terms of frequency makes its analysis easier through curves. PRECAUTIONS: 1. Choice of dependent and independent variables should be made aptly. 2. Data variables for frequency curves should be decided before hand for proper results. 27

28 OBJECTIVE 7: Descriptive statistics. INPUTS: File: group1.sav PROCEDURE FOLLOWED: COMMANDS: 1. Analyze 2. Descriptive Statistics 3. Frequencies OUTPUT: Frequencies Statistics Height Gender 2nd sem marks N Valid Missing Mean Median

29 Mode 2 71 Std. Deviation Variance Skewness Std. Error of Skewness Kurtosis Std. Error of Kurtosis Range 1 65 Minimum 1 33 Maximum 2 98 Sum Frequency Tables Height Frequency Percent Valid Percent Cumulative Percent 5'1" '2" '4" '5" '6" Valid 5'7" '8" '9" '95" ' '1"

30 Total Gender Frequency Percent Valid Percent Cumulative Percent Valid Total Histogram 30

31 OUTPUT: CONCLUSION: 1. Frequency tables show us vivid statistical interpretation of data. 2. Frequency curves show us easy interpretation of skewness and kurtosis curves. PRECAUTIONS: 1. Curves of symmetry should be judged carefully. 2. Do note that quartile divides distribution into 4 equal parts. 31

32 OBJECTIVE 8: Correlation and Regression INPUTS: Files: group1.sav,group12.sav PROCEDURE FOLLOWED: Correlation : COMMANDS: 1. Analyze 2. Correlate 3. Bivariate OUTPUT: Correlations Descriptive Statistics Mean Std. Deviation N Weight st sem marks Gender

33 Correlations Weight 1st sem marks Gender Pearson Correlation ** Sig. (2-tailed) Weight Sum of Squares and Crossproducts Covariance N Pearson Correlation Sig. (2-tailed) st sem marks Sum of Squares and Crossproducts Covariance N Pearson Correlation.507 ** Sig. (2-tailed) Gender Sum of Squares and Crossproducts Covariance N **. Correlation is significant at the 0.01 level (2-tailed). Non-parametric Correlations Correlations Weight 1st sem marks Gender Kendall's tau_b Weight Correlation Coefficient *.414 * 33

34 Sig. (2-tailed) N Correlation Coefficient * st sem marks Sig. (2-tailed) N Correlation Coefficient.414 * Gender Sig. (2-tailed) N Correlation Coefficient *.487 * Weight Sig. (2-tailed) N Correlation Coefficient * Spearman's rho 1st sem marks Sig. (2-tailed) N Correlation Coefficient.487 * Gender Sig. (2-tailed) N *. Correlation is significant at the 0.05 level (2-tailed). Regression A. Linear COMMANDS: 1. Analyze 2. Regression 3. Linear 34

35 OUTPUT: Regression Descriptive Statistics a Mean Std. Deviation N Weight AIEEE marks st sem marks nd sem marks a. Selecting only cases for which Computer = 1 Correlations a Weight AIEEE marks 1st sem marks 2nd sem marks Weight Pearson Correlation Sig. (1-tailed) N AIEEE marks st sem marks nd sem marks Weight AIEEE marks st sem marks nd sem marks Weight AIEEE marks st sem marks

36 a. Selecting only cases for which Computer = 1 2nd sem marks Variables Entered/Removed a,b Model Variables Variables Method Entered Removed 2nd sem marks, 1 AIEEE marks, 1st. Enter sem marks c a. Dependent Variable: Weight b. Models are based only on cases for which Computer = 1 c. All requested variables entered. Model Summary Model R R Square Adjusted R Computer = 1 Square (Selected) Std. Error of the Estimate a a. Predictors: (Constant), 2nd sem marks, AIEEE marks, 1st sem marks ANOVA a,b Model Sum of Squares df Mean Square F Sig. Regression c 1 Residual Total a. Dependent Variable: Weight b. Selecting only cases for which Computer = 1 c. Predictors: (Constant), 2nd sem marks, AIEEE marks, 1st sem marks Coefficients a,b Model Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig. (Constant) AIEEE marks st sem marks nd sem marks

37 a. Dependent Variable: Weight b. Selecting only cases for which Computer = 1 B. Curve fit: COMMANDS: 1. Analyze 2. Regression 3. Curve Estimation OUTPUT: Curve Fit Model Description Model Name MOD_1 Dependent Variable 1 Weight Equation 1 Linear Independent Variable 12th class marks Constant Included Variable Whose Values Label Observations in Plots Unspecified Case Processing Summary Total Cases 64 N 37

38 Excluded Cases a 0 Forecasted Cases 0 Newly Created Cases 0 a. Cases with a missing value in any variable are excluded from the analysis. Variable Processing Summary Variables Dependent Weight Independent 12th class marks Number of Positive Values Number of Zeros 0 0 Number of Negative Values 0 0 User-Missing 0 0 Number of Missing Values System-Missing 0 0 Dependent Variable: Weight Model Summary and Parameter Estimates Equation Model Summary Parameter Estimates R Square F df1 df2 Sig. Constant b1 Linear The independent variable is 12th class marks. 38

39 CONCLUSION: We conclude that weight has no effect on aieee marks or 12 th marks etc. as correlation coefficient is near about zero but in aieee marks have a great effect on 12 th marks as coefficient of correlation is more. Correlation coefficient formula: PRECAUTIONS: 1. Variables should be chosen properly. 2. It may not be the exact result so it should be properly decided before hand on what variables are to be correlated. 39

40 OBJECTIVE 9: Chi square test INPUTS: File: group1.sav PROCEDURE FOLLOWED: Independent: COMMANDS: 1. Analyze 2. Nonparametric test 3. Legacy dialogs 4. Chi square OUTPUT: Chi-Square Test Frequencies Gender Observed N Expected N Residual Total

41 Test Statistics Gender Chi-Square.264 a df 1 Asymp. Sig..608 a. 0 cells (0.0%) have expected frequencies less than 5. The minimum expected cell frequency is 7.8. Dependent: COMMANDS: 1. Analyze 2. Descriptive statistics 3. Cross tabs OUTPUT: Crosstabs Case Processing Summary 41

42 Cases Valid Missing Total N Percent N Percent N Percent Gender * Computer % % % Gender * Computer Crosstabulation Computer Total no yes Count Expected Count female % within Gender 55.6% 44.4% 100.0% % within Computer 62.5% 28.6% 40.9% Gender % of Total 22.7% 18.2% 40.9% Count Expected Count Male % within Gender 23.1% 76.9% 100.0% % within Computer 37.5% 71.4% 59.1% % of Total 13.6% 45.5% 59.1% Count Expected Count Total % within Gender 36.4% 63.6% 100.0% % within Computer 100.0% 100.0% 100.0% % of Total 36.4% 63.6% 100.0% Chi-Square Tests Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) 42

43 Pearson Chi-Square a Continuity Correction b Likelihood Ratio Fisher's Exact Test Linear-by-Linear Association N of Valid Cases 22 a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is b. Computed only for a 2x2 table CONCLUSION: Chi square formula: Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis. PRECAUTIONS: 1. Variables should be chosen properly. 2. It may not show proper results so what we have to observe should be decided before hand. 43

44 OBJECTIVE 10: T test INPUTS: Files: group1.sav PROCEDURE FOLLOWED: A. One way: COMMANDS: 1. Analyze 2. Compare means 3. One sample t test OUTPUT: (i) T-Test(Test value = 50) One-Sample Statistics N Mean Std. Deviation Std. Error Mean Weight One-Sample Test Test Value = 50 T df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper Weight

45 (ii) T-Test (Test value = 70) One-Sample Statistics N Mean Std. Deviation Std. Error Mean Weight One-Sample Test Test Value = 70 T df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper Weight B. Paired: COMMANDS: 1. Analyze 2. Compare means 3. Paired-samples mean test OUTPUT: T-Test 45

46 Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 12th class marks AIEEE marks Paired Samples Correlations N Correlation Sig. Pair 1 12th class marks & AIEEE marks Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper 12th class marks - AIEEE Pair 1 marks CONCLUSION: T test formula: We concluded that as we increase the test value, mean difference decreases. It means that more approximately we estimate better result we get. PRECAUTIONS: 1. Variables should be chosen properly. 46

47 2. It may not show proper results so what we have to observe should be decided before hand. 47

48 OBJECTIVE 11: ANOVA test INPUTS: File: group1.sav, group12.sav PROCEDURE FOLLOWED: A. One way: COMMANDS: 1. Analyze 2. Compare means 3. One way ANOVA OUTPUT: A. One way ANOVA Sum of Squares df Mean Square F Sig. Between Groups st sem marks Within Groups Total nd sem marks Between Groups Within Groups

49 Total B. Two way COMMANDS: 1. Analyze 2. General legal model 3. Univariate OUTPUT: Univariate Analysis of Variance Between-Subjects Factors N st sem marks

50 nd sem marks Dependent Variable: 12th class marks Tests of Between-Subjects Effects Source Type III Sum of Squares df Mean Square F Sig. Intercept Hypothesis

51 @1stsemmarks Error... a Hypothesis Error... a Hypothesis Error... a Hypothesis Error... a a. Cannot compute the appropriate error term using Satterthwaite's method. Expected Mean Squares a,b Source Variance Component Var(@2ndsemm Var(@1stsemmar Var(Error) Quadratic Term arks) ks Intercept Error a. For each source, the expected mean square equals the sum of the coefficients in the cells times the variance components, plus a quadratic term involving effects in the Quadratic Term cell. b. Expected Mean Squares are based on the Type III Sums of Squares. CONCLUSION: Anova formula: 51

52 ANOVA is used to compare the means of three or more groups to determine whether they differ significantly from one another. Another important function is to estimate the differences between specific groups. The most common method to detect differences among groups in one-way ANOVA is the F-test, which is based on the assumption that the populations for all samples share a common, but unknown, standard deviation. We recognized, in practice, that samples often have different standard deviations. PRECAUTIONS: 1. Variables should be chosen properly. 2. It may not show proper results so what we have to observe should be decided before hand. 52