1. Introduction to Data Analysis for Managerial Decision

2. Statistics
Statistics is the science and art that transforms numbers or data into useful information for decision makers. It enables about the risks associated with decision making in business and reduce the variation in the decision making process.

3. Inferential Statistics Descriptive Statistics
Data
Qualitative Data
Quantitative data
Discrete Data
Continuous Data

4. Descriptive statistics: Descriptive statistics focuses on collecting, summarizing, presenting and analyzing a set of data. For example presentation of data in table and charts and statistics such as mean, median, mode, standard deviation, frequency, percent etc.
Inferential statistics: Inferential statistics uses data that have been collected from a small group to draw conclusion about a larger-group. These statistics is used in decision making process. Such as which investment might lead to higher return and what marketing strategy might lead to increased sales.

5. Types of Data and variables
Variable: A variable is a characteristics or attributes of an item or individual.
Data: Collected information under variable for the study is called data
Note that data are always associated with variable
For example:
Age
Gender 35
Male 1 45
Female 2 23 68 55
Age is called variable and values under age are called data.
Note that: Single value is called data point or datum and collection of data is called data set. Statistical analysis will be used only in data set to draw conclusion

6. Categorical Data and Variables: Data which provides only the categories and have no any numerical meaning are called categorical or qualitative data and variable associated with these data is called categorical/qualitative variable. For example: Gender of people, types of organization etc.
Numerical Data and variables: Data which gives numerical sense are called numerical/quantitative data.
For example: Age, income, year of education, year of experience, temperature etc.
Discrete Data and variables: The quantitative variable which takes only the whole number/rounded form data are called discrete data. Generally, discrete data will be generated through counting process. For example; family size, class size
Continuous Data and variables: The quantitative variable which takes the value in the range i.e. real number (whole number and fractional number) are called continuous data. Generally, continuous data will be generated through measuring process. For example; age, weight, height, income etc.

7. Measurement Scale of Data
Nominal Scale
Ordinal Scale
Interval Scale
Ratio Scale

8. Nominal scale:
The lowest level of data measurement is the nominal level. Categorization is the main purpose of this measurement scale. Number representing nominal data can be used only to classify or categorize.
Data from a categorical variable are measured on a nominal scale.
Some examples of nominal scale:
Gender of the people: 1. Male
2. Female
3. Third gender
Religion of the people: 1. Hindu
2. Buddhist
3. Muslim
4. Christian
5. Other religion
Type of magazines reader: 1. News magazines
2. Sports magazines
3. Movie magazines
4. Other type of magazine

9. Ordinal Scale:
Ordinal scale classifies data into distinct categories in which ranking is implied.
Ordinal level data measurement is higher than the nominal level. Ordinal level measurement is used to rank or order objects.
Some examples of ordinal scale:
Your income : 1. Rs. 0 – 1000
2. Rs – 2000
3. Rs – 3000
4. Rs and above
Your level of performance in job: 1. Above average
2. Average
3. Below average
Your level of satisfaction in job: 1. Strongly Satisfy
2. Somewhat satisfy
3. Somewhat dissatisfy
4. Strongly dissatisfy

10. Interval and Ratio Scales:
Data from quantitative variable are measured on an interval or a ratio scale. These are the highest scale of measurement.
The basic difference between the interval and ratio scale is : in interval scale there is no true zero value (only arbitrary zero value) whereas in ratio scale there is true zero value.
For examples:
Temperature, IQ test are some examples of interval scale
Height, length, income are some examples of ratio scale.

11. Year of Professional experience
Age
Gender
Year of education
Income
Residence
Expenditure
Year of Professional experience

12. Survey Questionnaire

13. Survey Questionnaire on Basic Information
Questionnaire No.
A1. Residence: [SA]
Rural
Urban
A2. Ecological Region
Mountain
Hill
Tarai 1 2 3
A3. Development Region
EDR
CDR
WDR
MWDR
FWDR 1 2 3 4 5
A4. Sex: [SA]
Female
Male
A5. How many members (residing permanently) are there in your family? …5………
A6. Age: ___24_______ [Completed age in years]

14. A7. Among the various incomes generating an activity which is the most income generating activity?
Agriculture 1
Industry/Business 2
Service in the country 3
Remittance (service outside the country) 4
Wage-labor in the locality 5
Retirement pension 6
Other (specify) Xx
A8. How many members of your household contributing to your household income?
A9. What is your mark in the midterm exam of Data Analysis for managerial decisions?
A10. In which program did you completed your undergraduate degree?
Management 1
Science 2
Humanities 3
Education 4

15. A11. What is the monthly income of your household? ……20,000……….
A12. While considering all things, what is the average monthly expenditure of your family/HH? , (InRs.)
A13. Except the college time, on an average how many hours you study in a day? ……2………

16. Data Spreadsheet QN A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 1 2 324 32
20000 4 23 7 56
55000
50000 5 29
40000
25000 6
36000
30000 26 27
13000
10000 52
45000 28 58
100000 8
200000
180000 9 21 36 10 37 11 51
44000 12
70000 13 18 35
150000 14 22 60
35000 15 45 16 17 57
28000 49 19 30 40
90000 20 25 55
60000

17. Data Analysis: Univariate Data Analysis: 2. Bivariate Data Analysis:
3. Multivariate Data Analysis
Gender
Income
Expenditure
Gender
Expenditure
Gender
Expenditure
Income

18. Univariate data analysis:
In univariate data analysis researcher analyzed single variable at time.
For qualitative variables:
The researcher analyzed qualitative variable by finding the frequency and percent distribution for each category.
Table, pie chart, bar-char and side by side bar chart can be used to present the findings of such variables.
For quantitative variables:
The researcher can analyzed the quantitative variables by finding descriptive statistics such as mean, mode, median, and standards deviation. The most widely used tools are mean and standard deviation of the variable. While presenting the finding of this variables: tables, Histogram, box and whisker plot, and bar diagram can used according to the nature of the findings.

19. Bivariate Data Analysis:
In bivariate data analysis researcher analyzed two variables at a time to find the cause and effect of one variable to other variable. Under this technique various statistical tools can be applied such as covariance, correlation coefficients, t-test, z-test, chi-square test, simple regression, binary logistic regression, one way ANOVA etc. While doing the bivariate data analysis caution should be taken to select the appropriate tools of statistics since which statistical tool is appropriate is based on measurement scale of data (nominal, ordinal, interval, and ratio scale).

20. Multivariate Data Analysis:
In multivariate data analysis researcher analyzed three or more variables at a time to find the cause and effect of one set of variables to other variables. Under this technique various statistical tools can be applied such as multiple correlation coefficients, partial correlation coefficients, multiple regressions, binary logistic regression, two way ANOVA, ANCOVA, MANOVA etc. While doing the multivariate data analysis caution should be taken to select the appropriate tools of statistics since which statistical tool is appropriate is based on measurement scale of data (nominal, ordinal, interval, and ratio scale).

21. Data analysis: Frequency analysis
Gender
Percent
Female
50.1
Male
49.9
Total
100.0
Age Group
Percent
20-29
33.9
30-39
24.7
40-49
17.4
50-59
11.9
60-69
7.5
70-79
3.6
80+ 1
Total
100.0

22. Generally speaking, do you think the country is moving in the right direction, or do you think that it is moving in the wrong direction?
Frequency
Percent
1. Right direction
764
25.4
2. Wrong direction
1202
39.9
3. Some in right, some in wrong direction
526
17.5
4. Refused 5
0.2
5. Don't know/cannot say
513
17.0
Total
3010
100.0

23. Cross-tabulation
Cross-tabulation examines the relationship between two variables
(sometimes between more than two variables).
Cross-tabulation is usually between independent and dependent
variables.
Attitudes, views and opinions are dependent variables while attributes
such as sex, ethnicity, region, political persuasion, and demographic
characteristics such age, education and income status are independent
This is subjected to ordinal and nominal scales.

24. Some examples of cross-tabulation:
Generally speaking, do you think the country is moving in the right
direction, or do you think that it is moving in the wrong direction?
Cross-tabulated by Ecological Region
Mountain
Hill
Tarai
All
1. Right direction
43.0
29.4
20.0
25.4
2. Wrong direction
12.7
28.0
53.6
39.9
3. Some in right, some in wrong direction
13.3
22.2
13.7
17.5
4. Refused
0.0
0.1
0.2
5. Don't know/cannot say
30.9
20.3
12.6
17.0
Total
100.0

25. Descriptive analysis
Descriptive analysis includes the calculation of measure of central
tendency (mean, median and mode) and measure of dispersion
(variance, standard deviation etc.).
This analysis is subjected to ratio scale only.
Examples:
Mean age = 34
Mean income = Rs. 10,000
Mean weight = 56 kg.

26. Visualization of Data The Bar Chart The Pareto Chart The Pie Chart
The side-by-side Bar Chart

27. Who determines the price of your product? [Base = 1023]
The Bar Chart
B9. Who determines the price of your product?
Frequency
Percent
Market
775
75.8
Middle men 26
2.5
Syndicate
200
19.6
Regulating agency 22
2.2
Total
1023
100.0
Who determines the price of your product? [Base = 1023]

28. Who determines the price of your product? [Base = 1023]
The Pie Chart
B9. Who determines the price of your product?
Frequency
Percent
Market
775
75.8
Middle men 26
2.5
Syndicate
200
19.6
Regulating agency 22
2.2
Total
1023
100.0
Who determines the price of your product? [Base = 1023]

29. Difference between Bar and Pareto Chart
Who determines the price of your product? [Base= 1023]
Bar Chart
Pareto Chart

30. The Side-by-Side Bar Chart (Multiple Bar Chart)
The findings which are presented in contingency table can be presented using side-by-side bar chart
Who determines the price of your product? [Base= 1023]
Scale of Business
All
Micro Scale Business
Small Scale Business
Medium Scale Business
Market 76 79 77 62
Middle men 3 2 4
Syndicate 20 17 28
Regulating agency 1 6
Total
100.0

31. The Side-by-Side Bar Chart
Who determines the price of your product? [Base= 1023]