# Desccriptive Statistics

**Topics:**Regression analysis, Linear regression, Statistics

**Pages:**11 (2325 words)

**Published:**April 15, 2013

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 variables are as follows-

Descriptive summary for Annual Income|

INCOME| Statistic| Std. Error|

| Mean| 43.48| 2.058|

| 95% Confidence Interval

| Lower Bound| 39.34| |

| | Upper Bound| 47.62| |

| 5% Trimmed Mean| 43.42| |

| Median| 42.00| |

| Variance| 211.724| |

| Std. Deviation| 14.551| |

| Minimum| 21| |

| Maximum| 67| |

| Range| 46| |

| Interquartile Range| 25| |

| Skewness| .096| .337|

| Kurtosis| -1.248| .662|

Descriptive Statistics for Household Size|

Household Size| Statistic| Std. Error|

| Mean| 3.42| .246|

| 95% Confidence Interval for Mean| Lower Bound| 2.93| | | | Upper Bound| 3.91| |

| 5% Trimmed Mean| 3.36| |

| Median| 3.00| |

| Variance| 3.024| |

| Std. Deviation| 1.739| |

| Minimum| 1| |

| Maximum| 7| |

| Range| 6| |

| Interquartile Range| 3| |

| Skewness| .528| .337|

| Kurtosis| -.723| .662|

Descriptive statistics for Annual Credit Charged|

Annual Credit Charged| Statistic| Std. Error|

| Mean| 3964.06| 132.016|

| 95% Confidence Interval for Mean| Lower Bound| 3698.76| | | | Upper Bound| 4229.36| |

| 5% Trimmed Mean| 3971.48| |

| Median| 4090.00| |

| Variance| 871411.200| |

| Std. Deviation| 933.494| |

| Minimum| 1864| |

| Maximum| 5678| |

| Range| 3814| |

| Interquartile Range| 1638| |

| Skewness| -.130| .337|

| Kurtosis| -.742| .662|

From the above 3 tables we get to know that the average annual income of the households is 43.48 and the average household size is 3.42 along with the average amount charged as 3964. With the 95% confidence interval the mean for all the three variables happens to be easily estimated and the interval is definitely narrower. The variability from the mean in the annual income is 211.724 which tell us that the risk incurred in the annual...

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