rm file for bba PDF

Preview

Summary

Fairfield Institute of Management and TechnologyResearch Methodology Lab208NAAC ACCREDITEDSubmitted to: Submitted by:Dr. Suman Tarun YadavEnrolment No:41990101718BBA(G)4 th Sem/ Sec BTable Of ContentSl No.Lab Exercise Topic Page No. Signature Introduction to data analysis 3 Creating Charts in MS Exc...

Description

Fairfield Institute of Management and Technology Research Methodology Lab 208

NAAC ACCREDITED

Submitted to: Dr. Suman

Submitted by: Tarun Yadav Enrolment No: 41990101718 BBA(G) 4th Sem/ Sec B

Table Of Content Sl No.

Lab Exercise Topic

Page No.

1.

Introduction to data analysis

3

2.

Creating Charts in MS Excel, Bar Chart, Pie Chart

4

3.

Calculating the Pearson Correlation Coefficient with Excel

7

4.

T-test- Introduction &T-test on excel

9

5.

ANOVA- Introduction

12

Signature

Lab Assignment I INTRODUCTION TO DATA ANALYSIS

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decisionmaking. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, while being used in different business, science, and social science domains. In today’s business, data analysis is playing a role in making decisions more scientific and helping the business achieve effective operation. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.

Data integration is a precursor to data analysis,[according to whom?] and data analysis is closely linked[how?] to data visualization and data dissemination. The term data analysis is sometimes used as a synonym for data modeling

LAB ASSIGNMENT 2 Creating Charts in M.S. Excel: Graphic Displays for Qualitative Data Introduction: Graphically representing data is one of the most helpful ways to become acquainted with the sample data. In this lab you will use Excel to present data graphically. You will be analysing data using types of graphs: Circle graphs, Bar graphs

I.

BAR CHART

The Bar Chart is like a Column Chart lying on its side. The horizontal axis of a Bar Chart contains the numeric values. The first chart below is the Bar Chart for our single series, Flowers. When to use a Bar Chart versus a Column Chart depends on the type of data and user preference. Sometimes it is worth the time to create both charts and compare the results. However, Bar Charts do tend to display and compare a large number of series better than the other chart types. A bar chart or bar graph is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a column bar chart. Bar charts provide a visual presentation of categorical data. Categorical data is a grouping of data into discrete groups, such as months of the year, age group, shoe sizes, and animals. In a column bar chart, the categories appear along the horizontal axis; the height of the bar corresponds to the value of each category.

Exercise. 12 students were given intensive couching and 2 tests were conducted in a different year. This course of test English & Accounts are given below: 2002

2003

2004

2005

2006

2007

2008

2009

2010

36

57

43

36

62

77

49

84

87

94

Accounts 43

66

76

83

63

82

74

64

87

96

Year English

2001

Let’s continue to use the data from Exercise 1, and present this data as a bar graph. Since we already have the data entered, we can go right to the commands to create the bar graph: Highlight the data. From the ribbon, select the Insert tab > Column> 2D

II.CIRCLE GRAPHS A circle graph shows the amount of data that belongs to each category as a proportional part of a circle. Consider Example 1. We are instructed to construct a circle graph, with data presented as a frequency distribution. Enter the data (either by hand, or opening the data file.) Highlight the data. From the ribbon, select the Insert tab > Pie > 2D pie

Lab Assignment 3 Calculating the Pearson Correlation Coefficient with Excel Correlation means association - more precisely it is a measure of the extent to which two variables are related. There are three possible results of a correlational study: a positive correlation, a negative correlation, and no correlation. A positive correlation is a relationship between two variables in which both variables either increase or decrease at the same time. An example would be height and weight. Taller people tend to be heavier. A negative correlation is a relationship between two variables in which an increase in one variable is associated with a decrease in the other. An example would be height above sea level and temperature. As you climb the mountain (increase in height) it gets colder (decrease in temperature). A zero correlation exists when there is no relationship between two variables. For example there is no relationship between the amount of tea drunk and level of intelligence. A correlation can be expressed visually. This is done by drawing a scatter gram - that is one can plot the figures for one variable against the figures for the other on a graph. Some uses of Correlations: Prediction, Validity. Reliability, Theory verification Predictive validity. Correlation coefficient(s) with Excel 1.Datatab→Data Analysis→ Correlation (or Insert Function – Statistical) 2. Highlight all the columns containing variables you suspect are correlated. 3. Check Labels in First Row only if you highlighted the top row labels. 4. Click the Output Range: and select a place for the output by clicking on a cell. It will create annxn array wheren is the number of variables (columns). 5. Click OK and this will produce an array of correlation coefficients between all of the variables represented by the columns. It’s good for seeing which of many variables are most strongly correlated.

Some uses of Correlations: Prediction, Validity. Reliability, Theory verification Predictive validity

Lab Assignment 4 T-test- Introduction &T-test on excel

T-TEST A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. It is mostly used when the data sets, like the data set recorded as the outcome from flipping a coin 100 times, would follow a normal distribution and may have unknown variances. A t-test is used as a hypothesis testing tool, which allows testing of an assumption applicable to a population.

A t-test looks at the t-statistic, the t-distribution values and the degrees of freedom to determine the probability of difference between two sets of data. To conduct a test with three or more variables, one must use an analysis of variance. Two-Sample T-Test Assumptions The assumptions of the two-sample t-test are: 1. The data are continuous (not discrete). 2. The data follow the normal probability distribution. 3. The variances of the two populations are equal. (If not, the Aspin-Welch Unequal-Variance test is used.) 4. The two samples are independent. There is no relationship between the individuals in one sample as compared to the other (as there is in the paired t-test). 5. Both samples are simple random samples from their respective populations. Each individual in the population has an equal probability of being selected in the sample

T-TEST ON EXCEL 14 persons were appointed in an officer grade in an office their performance is noted by giving a test and marks are out of 100. They were given 4 months training and test. By using t-test conclude that employees have benefits by training or not. The data as follows

The First Step Is To Develop The Hypothesis That Is H 0 Which Means The Null Hypothesis And Also, We Have To Make H1 Which Means Alternative Hypothesis. H0 = The Training Is Not Beneficial H1 = The Training Is Beneficial Significance Level: 5% Selection Of Test: In This Case We Will Use T-Test As There Is Less Than 30 Observations STEPS ON EXCEL: 1)Click on data on the menu bar 2)Click on data analysis and then select t-test: paired two sample for means 3)Following dialogue box will appear

4)Fill the input with the cells and select the cell where the output is to be shown 5)Click OK 6)Following is the table you will get

CONCLUSION: T CAL VALUE: TAB VALUE 2.44: 2.16 2.44 >2.16

AS WE CAN SEE THAT TAB VALUE IS LESS THAN T CALCULATED VALUE, THAN ACCEPT ALTERNATIVE HYPOTHESIS

Lab Assignment 5 ANOVA ON EXCEL

Introduction: ANOVA An ANOVA test is a way to find out if survey or experiment results are significant. In other words, they help you to figure out if you need to reject the null hypothesis or accept the alternate hypothesis. Basically, you’re testing groups to see if there’s a difference between them.

Examples of when you might want to test different groups: 

 

A group of psychiatric patients are trying three different therapies: counselling, medication and biofeedback. You want to see if one therapy is better than the others. A manufacturer has two different processes to make light bulbs. They want to know if one process is better than the other. Students from different colleges take the same exam. You want to see if one college outperforms the other.

ANOVA ON EXCEL

The First Step Is to Develop The Hypothesis That Is H 0 Which Means The Null Hypothesis And Also, We Have To Make H1 Which Means Alternative Hypothesis.

H0 = THE TRAINING IS NOT BENEFITIAL H1 = THE TRAINING IS BENEFICIAL SIGNIFICANCE LEVEL: 5% STEPS ON EXCEL: 1)Click on data on the menu bar 2)Click on data analysis and then select ANOVA: Single Factor 3)Following dialogue box will appear

4)Fill the input with the cells and select the cell where the output is to be shown 5)Click OK

6)Following is the table you will get

CONCLUSION: T CAL VALUE: TAB VALUE 2.13: 2.92 2.13...