Business statistics is the science of good decision making in the face of uncertainty and is used in many disciplines such as financial analysis, econometrics, auditing, production and operations including services improvement, and marketing research.
These sources feature regular repetitive publication of series of data. This makes the topic of time series especially important for business statistics. It is also a branch of applied statistics working mostly on data collected as a by-product of doing business or by government agencies. It provides knowledge and skills to interpret and use statistical techniques in a variety of business applications. A typical business statistics course is intended for business majors, and covers statistical study, descriptive statistics (collection, description, analysis, and summary of data), probability, and the binomial and normal distributions, test of hypotheses and confidence intervals, linear and multiple regression, and correlation.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to forecast future events based on known past events to predict data points before they are measured. Time series are very frequently plotted via line charts.
Regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes due to a change in the independent variables.
In this report, we tried to show the practical uses of Multiple Regression and Time Series using share prices and other information associated with it like Net income, Dividend per Share, Return on Equity etc. We tried to show impact due to the changes of these independent variables (Net income, Dividend per Share, Return on Equity) on the dependent variable share price.
These sources feature regular repetitive publication of series of data. This makes the topic of time series especially important for business statistics. It is also a branch of applied statistics working mostly on data collected as a by-product of doing business or by government agencies. It provides knowledge and skills to interpret and use statistical techniques in a variety of business applications. A typical business statistics course is intended for business majors, and covers statistical study, descriptive statistics (collection, description, analysis, and summary of data), probability, and the binomial and normal distributions, test of hypotheses and confidence intervals, linear and multiple regression, and correlation.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to forecast future events based on known past events to predict data points before they are measured. Time series are very frequently plotted via line charts.
Regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes due to a change in the independent variables.
In this report, we tried to show the practical uses of Multiple Regression and Time Series using share prices and other information associated with it like Net income, Dividend per Share, Return on Equity etc. We tried to show impact due to the changes of these independent variables (Net income, Dividend per Share, Return on Equity) on the dependent variable share price.
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