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Business Statistics is mainly concerned with taking the better decisions under the definite conditions. It uses various techniques to acquire the significant outcomes. It is further 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. Making 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. Few 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
So far we know that writing an assignment on the Business Statistics can be daunting for students. It requires the practical knowledge and theoretical knowledge. At this time, they want an expert helps to get the good grades in their Business Statistics assignment. So, students of you are in the same situation then you can hire our experts to complete your assignment. Some of the benefits of choosing our services are as follows:
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Our experts are capable to provide the assignment help on any complex topics of Business Statistics, such as Business Statistical Quality Control, Statistical Decision Theory, Liner Programming, Business Forecasts, Correlation Analysis, Regression Analysis, Time Series Analysis, etc.
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Topics for Introductory business statistics Assignment help :
- Data summaries, Descriptive statistics, Introduction to a statistical computer package, Probability, Distributions, Expectation, Variance, Covariance, Portfolios, Central limit theorem, Statistical inference of univariate data, Statistical inference for bivariate data, Linear simple regression models, Multiple regression, Model selection, Analysis of variance, Linear logistic regression, Introduction to time series, Business applications, Economic Theory, statistical concepts, problem-solving techniques, visual methods, numerical methods, hypothesis testing, literature in economics, accounting, problems in accounting, economics, Finance, Descriptive statistics, Probability theory, Probability distributions, Confidence interval estimation, Hypothesis testing, descriptive statistics
- Graphical methods, measures of central tendency, measures of central variation, probability, probability distributions, sampling distributions, correlation, regression, statistical computer applications, computation of probabilities, interpretation of a histogram, probability rules, combinatorial probability, probability distributions, sampling distributions, absolute addressing, intrinsic functions, Statistical Analysis,
- Counting, Probability,The Normal Distribution,Sampling and Sampling Distributions,Estimation and Hypothesis Testing,Correlation and Regression,data displays (including boxplots, histograms,scatterplots), summary statistics (including mean, standard deviation), uncertainty ,probability as limiting relative frequency, the normal distribution as a reference standard,statistical estimation and tests, standard errors, confidence intervals, p-values
Complex topics covered by Introductory business statistics Assignment experts :
- Standard error, central limit theorem ,Confidence intervals and tests ,Two-sample comparison ,Chi-squared, dependence, and Simpson’s paradox , Linear patterns ,Residual analysis ,Data transformations ,Simple Regression Model ,Inference and prediction in the SRM ,Regression diagnostics ,Dependence and time series ,Multiple Regression Model ,Inference in multiple regression ,Collinearity in multiple regression , Building regression models
- Categorical variables in regression ,Interactions in regression ,More complex categorical features,Trends in time series ,Models for time series ,Categorical responses in regression, Calculus and probability,Economics for business,Introduction to financial and management accounting,Mathematics for actuaries,Programming for applications,Applied statistics ,Business finance,Differential equations and applied methods,Financial accounting,Financial mathematics
- Mathematical statistics,Advanced statistics,Descriptive statistics - graphical descriptive methods,Descriptive statistics - numerical descriptive methods,Discrete and continuous probability distribution,Introduction to statistical inference,Confidence interval estimators,Hypothesis testing,Correlation analysis and sample linear regression,Multiple regression model,Index numbers
Simple regression model, Multiple regression model, Sampling variation, Standard error, Central limit theorem , Confidence intervals and tests, Two-sample comparison, Chi-squared ,Simpson’s paradox , Linear patterns , Residual analysis, Data transformations