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Statistics
Md Muktadir Islam
ID: 2223031014
Analyzing Dataset using SPSS
Presented to
Syed Shahadat Hossain, Ph.D
Professor, ISRT, DU
Presented by
Md Muktadir Islam
ID: 2223031014
Variable Selection for Analysis Sample 9
Sample 9
Data View
Number of
Individual data: 57
Educational Level Skewness: .040/.316 = 0.126 (between -1.96 to +1.96)
Kurtosis: .066/.623 = 0.106 (between -1.96 to +1.96)
Lowest Education level 8 years
25% of the employee have 12 years of
Education
75% of the employee have 16 years
Education
Highest Education Level 21 years
Stem and leaf display of Education Level Box plot of Education Level
Right skewed
Beginning Salary
Skewness:
1.939/.316 = 6.136 (not between -1.96 to +1.96)
Kurtosis:
3.764/.623 = 6.041 (not between -1.96 to +1.96)
Mode 12
25% of the Employee get $ 12,450
as their Beginning Salary
75% of the Employee get $ 18,750
as their Beginning Salary
Box plot of Beginning Salary
Stem and leaf display of Beginning
Salary
left skewed
7 outliers
Correlation Analysis
 Education Level
 Beginning Salary
Independent Variable : Education Level
Dependent Variable : Beginning Salary
R Square= 43% , So 43% of the variability of Beginning Salary can be
explained through educational level
Coefficients of Determiner
Linear Relationship in Graphs
Regression Analysis
If the educational level increased by 1 year the beginning salary will be increased by
1757.943 dollars.
The educational level significantly influences the beginning salary
Let,
Ho: µ=21,000, Ha: µ < 21,000
Since the significance value is less than 0.05 , so it
can be said that there is enough evidence to reject
null hypothesis.
HypothesisTesting of Beginning Salary
Hypothesis Testing of Educational Level
Let,
Ho: µ=16, Ha: µ ≠16
Since the significance value is less than 0.05 , so it can be said that there
is enough evidence to reject null hypothesis.
Thank You

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STPP.pptx

  • 1. Statistics Md Muktadir Islam ID: 2223031014 Analyzing Dataset using SPSS Presented to Syed Shahadat Hossain, Ph.D Professor, ISRT, DU Presented by Md Muktadir Islam ID: 2223031014
  • 2. Variable Selection for Analysis Sample 9
  • 3. Sample 9 Data View Number of Individual data: 57
  • 4. Educational Level Skewness: .040/.316 = 0.126 (between -1.96 to +1.96) Kurtosis: .066/.623 = 0.106 (between -1.96 to +1.96)
  • 5.
  • 6. Lowest Education level 8 years 25% of the employee have 12 years of Education 75% of the employee have 16 years Education Highest Education Level 21 years Stem and leaf display of Education Level Box plot of Education Level Right skewed
  • 7. Beginning Salary Skewness: 1.939/.316 = 6.136 (not between -1.96 to +1.96) Kurtosis: 3.764/.623 = 6.041 (not between -1.96 to +1.96)
  • 9. 25% of the Employee get $ 12,450 as their Beginning Salary 75% of the Employee get $ 18,750 as their Beginning Salary Box plot of Beginning Salary Stem and leaf display of Beginning Salary left skewed 7 outliers
  • 10. Correlation Analysis  Education Level  Beginning Salary
  • 11. Independent Variable : Education Level Dependent Variable : Beginning Salary R Square= 43% , So 43% of the variability of Beginning Salary can be explained through educational level Coefficients of Determiner
  • 13.
  • 14. Regression Analysis If the educational level increased by 1 year the beginning salary will be increased by 1757.943 dollars. The educational level significantly influences the beginning salary
  • 15. Let, Ho: µ=21,000, Ha: µ < 21,000 Since the significance value is less than 0.05 , so it can be said that there is enough evidence to reject null hypothesis. HypothesisTesting of Beginning Salary
  • 16. Hypothesis Testing of Educational Level Let, Ho: µ=16, Ha: µ ≠16 Since the significance value is less than 0.05 , so it can be said that there is enough evidence to reject null hypothesis.