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Business Statistics mainly concerned with taking the better decisions under the definite conditions. It uses various techniques to acquire the significant outcomes. It used in the various fields of business, such as econometrics, marketing research, finance, banking, quality control, stock market, and many others areas. In business field, decision making is an important process. Make the right decision needs the collection of right information. This information is collected by Business Statistics in right manner. We also can analyze the data by using various statistics tools and the result that we get in last is more appropriate and reliable. Some important use of business Statistics are:

- Used practically in every zone of the business
- Provide relevant data to enhance the business activities

Business Statistics deals with the various topics, such as Chi-square tests, simple linear regression, multiple regression analysis, business forecasting methods, standard deviation, Comparison of variances, Normal Distribution, Sampling Distributions, and many more.

Statistical hypothesis is mainly based on population parameter. It is an assumption which is used for determine the parameter of population and this assumption can be true or not. One can check the statistical hypothesis is true or not by examining the whole population and if any kind of simple data occurs after the examining and these data are not consistent with Statistical hypothesis, then the hypothesis is not true and rejects. There are two types of statistical hypothesis, including Alternative hypothesis and Null hypothesis. Statistician determines the rejection of Null hypothesis by follows a process which is based upon sample data and this process is termed as Hypothesis testing. Hypothesis testing strategy involves the four major steps:

- State the hypotheses: this step involves the stating of both types of statistical hypothesis in a way that both are mutually exclusive to each other, and if one is true than other will be false.
- Make an analysis plan: this step involves a plan strategy in order to the evaluating the null hypothesis by using sample data.
- Analyzing data: in this step, values of the test static must be described.
- Generate outputs: in this, user applies all the plans that described in the analysis plan.

P-value approach determines two terms by observing the test static in the manner of alternative hypothesis and the terms that are determines by the P-value approach are likely and unlikely. P-value said to be unlikely, if its value is small and if its value is large then it said to be likely.

Multiple Regression is defined as a statistical tool that is used to determine the relationship of multiple independent variables with a dependent variable. By examining this relationship, a user can take the all information about the independent variables and use in the good way. Multiple Regression first state the null hypothesis and research hypothesis, collects the data, then user can assess the relationship between the independent variables. After that, it computes the regression data and examines the measures of association and then performs a last step which includes the rejection or acceptance of research hypothesis and null hypothesis.

Moreover, ANOVA uses F-tests to determine the equality of the groups whether they are same or different, whereas F-tests is termed as the ratio of two variances. But it has to be included the correct variances. In ANOVA, F-test is used in the following ratio:

F= variation among the means of samples/ variation with samples

Small Sample theory is the study of statistical inference in which small sample is given by n<=30. It involves F-distribution and t-distribution. T-distribution is the test of hypothesis about the correlation coefficient, differences among the two mean and population mean. F-distribution is the defined as the ratio of the variances of two distributed populations and its shape depend on the degree of freedom.

Frequency is concerned with the repetition of the number related to a particular research and these numbers are listed in a table, and this table known as frequency distribution table and the list of table is termed as frequency distribution. Frequency of a specific observation can be defined as the how many times a number occur in the data. Frequency distribution can be represented as histograms, polygons and frequency tables. Tables of the frequency distribution may be used for the numerical or categorical variables. There are various types of frequency distributions which are as follows:

- Relative frequency distribution
- Relative cumulative frequency distribution
- Cumulative frequency distribution
- Ungrouped frequency distribution
- Grouped frequency distribution

Business statistics field involves the various major topics in this which are Statistical Analysis, Counting, Probability, and Probability Distributions, Correlation and Regression, factor analysis, binary regression, nonparametric methods, multiple linear regression, analysis of variance and many more. Few progressive topics that also come in the Business Statistics field are as follows:

- Analysis of Time Series
- Regression Analysis
- Contingency and Probability
- Theoretical Frequency Distribution
- Interpolation and Extrapolation
- Association of Attributes

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**Statistics for business and economics**

- Problem definition, data and statistics, descriptive statistics
- Measurement
- Measures of central tendency
- Probability
- Probability
- Discrete probability distributions
- Continuous probability distributions
- Sampling distributions
- Interval estimation
- Hypothesis testing
- Statistical inference about means
- Statistical inferences
- About population variance
- Goodness of fit and independence
- Analysis of variance
- Simple regression
- Multiple regression
- Regression analysis: model building
- Index numbers
- Discrete probability distributions
- Continuous probability distributions
- Sampling distributions
- Estimation and hypothesis testing
- Simple linear regression and correlation
- Multiple regression
- Statistics and model building
- Analysis of variance
- Data description
- Descriptive statistics
- Hypothesis testing
- Regression basics
- Multiple regression
- Hypothesis testing
- Introduction to probability
- Discrete and continuous probability distributions
- Comparisons involving means
- Data and statistics
- Tabular and graphical methods
- Qualitative data
- Quantitative data
- Cumulative distributions
- Exploratory data analysis
- Measures of data location
- Mean, median, mode, quartiles
- Measures of data variability
- Range, variance, standard deviation,
- Coefficient of variation
- Z-scores and detection of outliers
- Covariance and correlation
- Weighted mean
- Probability
- Random variables
- Discrete probability distributions
- Expected value and variance
- Continuous probability distributions
- Normal probability distribution
- Using excel to compute normal

**STATISTICS FOR BUSINESS AND ECONOMICS**

- Introduction to statistical analysis theory
- Interpretation used in business and economics
- Probability and probability distributions; normal probability distribution; sampling distributions and parameter estimation; hypotheses testing; linear regression and correlation; time-series analysis and forecasting; index numbers.