Desccriptive Statistics
QM2 Project
Case Study 1- Consumer Characteristics
Index Sno Title Page.no 0 | Introduction | 3 | 1 | Summarizing data using Descriptive Statistics | 4-6 | 2
2.1
2.2 | Estimated regression equations.
Independent Variable- Annual Income.
Independent Variable- Household Size | 7
8
9 | 3 | Better predictor of annual credit card charges | 10 | 4 | Independent variables- Annual income and Household size | 11 | 5 | Forecasting Annual Credit Charge | 12 | 6 | Need for other independent variables | 13 | 7 | Test the significance of the overall regression model | 14 | 8 | Test the significance of the individual regression coefficients | 15-16 | 9 | Correlation Matrix | 17 | 10 | Coefficient of Determination | 18 | 11 | Brief Note | 19 |
Introduction
The project here is done on Multiple Regression for the case study Consumer Characteristics. Multiple Regressions is basically a statistical tool used to predict the values of one variable which is dependent on the values of two or more variables. In the case study Future View”, Inc., is an independent agency that conducts research on the consumer attitudes for a variety of firms. Factors such as the consumer characteristics are taken into account predict the amount charged by credit card users. Data was also collected on annual income, household size and annual credit card charges for a sample of 50 consumers.
1-Summarizing data using Descriptive Statistics
Descriptive statistics enlightens us with simple summaries about the observation and samples that have been done. This method can either be done on a population or for a sample. In the case study of consumer characteristics, data on the three variables given here are the income annual income, household size, and annual credit card charges for a sample of 50 consumers. With the help of IBM SPSS the descriptive statistics for these three
Case Study 1- Consumer Characteristics
Index Sno Title Page.no 0 | Introduction | 3 | 1 | Summarizing data using Descriptive Statistics | 4-6 | 2
2.1
2.2 | Estimated regression equations.
Independent Variable- Annual Income.
Independent Variable- Household Size | 7
8
9 | 3 | Better predictor of annual credit card charges | 10 | 4 | Independent variables- Annual income and Household size | 11 | 5 | Forecasting Annual Credit Charge | 12 | 6 | Need for other independent variables | 13 | 7 | Test the significance of the overall regression model | 14 | 8 | Test the significance of the individual regression coefficients | 15-16 | 9 | Correlation Matrix | 17 | 10 | Coefficient of Determination | 18 | 11 | Brief Note | 19 |
Introduction
The project here is done on Multiple Regression for the case study Consumer Characteristics. Multiple Regressions is basically a statistical tool used to predict the values of one variable which is dependent on the values of two or more variables. In the case study Future View”, Inc., is an independent agency that conducts research on the consumer attitudes for a variety of firms. Factors such as the consumer characteristics are taken into account predict the amount charged by credit card users. Data was also collected on annual income, household size and annual credit card charges for a sample of 50 consumers.
1-Summarizing data using Descriptive Statistics
Descriptive statistics enlightens us with simple summaries about the observation and samples that have been done. This method can either be done on a population or for a sample. In the case study of consumer characteristics, data on the three variables given here are the income annual income, household size, and annual credit card charges for a sample of 50 consumers. With the help of IBM SPSS the descriptive statistics for these three