Managerial Statistics Assignment help

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Data and Statistics

• Descriptive Statistics:, Tabular and Graphical Presentation, Descriptive Statistics: Numerical measure, Probability, Discrete Probability Distribution, Continuous Probability Distribution, Sampling, Sampling Distribution, Descriptive Statistics, Probability, Random Variables and their Distributions, and Normal Distribution , Sampling and Estimation.
• Sampling Distributions, and Estimation and Confidence Intervals, Linear Regression, simple linear regression, Multiple linear regression + dummy variables, model estimation, forecasting via regression, non-linear regression, Probabilistic Thinking, Descriptive Statistics, PivotTable, Basics of Probability, Conditional Probability, Expected Value and Variance of a Probability Distribution.
• Binomial Distribution, Decision Analysis, Normal Distribution, Normal, Basics of Portfolio Analysis, Correlation and Covariance, More Portfolios, Central Limit Theorem, Normal Approximation to the Binomial, Sampling Theory, Confidence Intervals for Single Populations, Student's t Distribution, Polling Examples, Confidence Intervals for Two Populations, Sample Size Selection, Basic Hypothesis Testing.
• Hypothesis Testing for Multiple Populations, Simple Regression, Multiple Regression, Multiple Regression: Model Building and Validation, Multicollinearity, Heteroscedasticity, Non-normality of Errors.

 

Topics for Managerial Statistics

 

• Descriptive Statistics, recap of descriptive statistics, mean, median, standard deviation, Normal approximation, Value-at-Risk, Normal approximation, financial data, regression line, correlation, causation , Random variables, expected value.
• variance, Normal Distribution, Normal distribution, normal table, linear transformations, hedging, portfolios, Sampling Distributions, Estimation, Confidence Intervals, Hypothesis Testing, Sampling proportions.
• interpreting opinion pollsHypothesis Testing, structure of a test for the population mean, Linear regression, simple linear regression, Multiple linear regression,