Business and Management Statistics Using Excel

Business and Management Statistics Using Excel, available at $64.99, has an average rating of 5, with 146 lectures, based on 2 reviews, and has 36 subscribers.

You will learn about Be able to analyze data graphically and quantitatively Be able to derive population information from samples Understand probability distributions and perform tests of hypotheses about the population based on sample information. Be able to formulate, estimate regressions using sample data, and perform tests of hypotheses about the population This course is ideal for individuals who are Undergraduates in schools of business and economics departments, engineering, political science, psychology, sociology, as well as MBA students. or Every undergraduate student business, economics, engineering, psychology, sociology, and political science is required to take a statistics course. This course offers a solid foundation for all undergraduates and MBA students. or For instance ANOVA, regression analysis, hypothesis testing, descriptive statistics such as means, medians, modes, variance, covariance, and correlation coefficient are widely used concepts. It is particularly useful for Undergraduates in schools of business and economics departments, engineering, political science, psychology, sociology, as well as MBA students. or Every undergraduate student business, economics, engineering, psychology, sociology, and political science is required to take a statistics course. This course offers a solid foundation for all undergraduates and MBA students. or For instance ANOVA, regression analysis, hypothesis testing, descriptive statistics such as means, medians, modes, variance, covariance, and correlation coefficient are widely used concepts.

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Summary

Title: Business and Management Statistics Using Excel

Price: $64.99

Average Rating: 5

Number of Lectures: 146

Number of Published Lectures: 145

Number of Curriculum Items: 146

Number of Published Curriculum Objects: 145

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Be able to analyze data graphically and quantitatively
Be able to derive population information from samples
Understand probability distributions and perform tests of hypotheses about the population based on sample information.
Be able to formulate, estimate regressions using sample data, and perform tests of hypotheses about the population

Who Should Attend

Undergraduates in schools of business and economics departments, engineering, political science, psychology, sociology, as well as MBA students.
Every undergraduate student business, economics, engineering, psychology, sociology, and political science is required to take a statistics course. This course offers a solid foundation for all undergraduates and MBA students.
For instance ANOVA, regression analysis, hypothesis testing, descriptive statistics such as means, medians, modes, variance, covariance, and correlation coefficient are widely used concepts.

Target Audiences

Undergraduates in schools of business and economics departments, engineering, political science, psychology, sociology, as well as MBA students.
Every undergraduate student business, economics, engineering, psychology, sociology, and political science is required to take a statistics course. This course offers a solid foundation for all undergraduates and MBA students.
For instance ANOVA, regression analysis, hypothesis testing, descriptive statistics such as means, medians, modes, variance, covariance, and correlation coefficient are widely used concepts.

Statistics consists of ways to analyze data and make informed decisions. However, to be able to deploy statistical methods appropriately and properly, one needs to have a firm understanding of the available statistical methodologies and continuously build on the basic information acquired from classes like this one. Therefore, statistics is a powerful tool needed by business managers, engineers, political scientists, medical and biological researchers in the data-driven world of today. Recent and evolving advances in information technology have expanded the role of data and “big data,” in all areas of endeavor, especially in the business, marketing, and medicine, among others. Huge volumes of the available data cannot be utilized properly unless they are summarized and visualized effectively. Business analytics provides methodologies that enable decision makers to effectively use large data sets that would not be practical by any other means. Those who are familiar with visualization tools like Tableau routinely encounter analysis that are statistics-based. This course teaches the basics of data analysis and summary statistics, visualization of data, pivot table, probabilities, hypothesis testing, ANOVA, and regression analysis. For instance, regression models may be used as basic tools of forecasting sales, costs, spread of diseases, among other uses. The tool we use is MS Excel. In most situations, once data are extracted using other tools such as SQL, Excel becomes the main platform for analysis.

Course Curriculum

Chapter 1: Chapter 1-Introduction

Lecture 1: Introduction

Lecture 2: The Course Textbook

Lecture 3: Overview of the course

Lecture 4: Introduction to random samples_1

Lecture 5: Introduction to random samples_2

Lecture 6: Introduction to random samples_3

Lecture 7: Introduction to random samples_4

Lecture 8: Introduction to regression_1

Chapter 2: Chapter 2 -Managing and Visualizing Data

Lecture 1: Data organization and visualization of the two variables together

Lecture 2: Data organization and visualization of the two variables together

Lecture 3: Data organization and visualization of the two variables together

Lecture 4: Data organization and visualization, histograms

Lecture 5: Data organization and visualization using histogram

Lecture 6: Data organization and visualization using histogram

Lecture 7: Data Analysis using pivot table

Lecture 8: Data analysis using pivot table

Chapter 3: Chapter 3- Numeric Descriptive Measures of Data

Lecture 1: descriptive measures #1

Lecture 2: descriptive measures#2

Lecture 3: descriptive measures#3

Lecture 4: descriptive measures#4

Lecture 5: descriptive_box and whisker

Lecture 6: descriptive measure_geometric average rate

Lecture 7: descriptive measure_geomteric average rate_example

Lecture 8: Variance and standard deviation

Lecture 9: Skewness and Kurtosis

Lecture 10: Standard error

Lecture 11: Covariance

Lecture 12: Correlation Coefficient

Lecture 13: The empirical rule

Lecture 14: Chebyshev's rule

Lecture 15: Standardization and Z score

Chapter 4: Section 4: Chapter 4-Probability Theory

Lecture 1: Contingency tables and probabilities

Lecture 2: Joint probability

Lecture 3: Marginal probability

Lecture 4: Conditional probability

Lecture 5: Probability of events A or B

Lecture 6: Independent events

Lecture 7: Bayes' Theorem _1

Lecture 8: Bayes' Theorem_2

Chapter 5: Section 5: Chapter 5- Discrete Probability Distributions

Lecture 1: Understanding expected values

Lecture 2: Variance is an expected value

Lecture 3: introduction to binomial distribution

Lecture 4: Binomial distribution and probabilities

Lecture 5: the expected value or mean of the binomial distribution

Lecture 6: Variance of the binomial distribution

Lecture 7: Cumulative Probability for binomial distribution

Lecture 8: Application of the binomial distribution to an example

Lecture 9: Introducing the Poisson distribution

Lecture 10: Histogram and the expected value of the Poisson distribution

Lecture 11: Variance of the Poisson distribution

Lecture 12: Application of the Poisson distribution, example

Chapter 6: Section 6: Chapter 6- Normal and Uniform Probability distributions

Lecture 1: Normal density function (ND)

Lecture 2: Standard normal density function (SND)

Lecture 3: SND and examples

Lecture 4: SND, going in reverse

Lecture 5: SND, going in reverse

Lecture 6: SND, going in reverse

Lecture 7: Checking normality using normal plot (QQ)

Lecture 8: QQ plot continued

Lecture 9: Uniform probability density function

Lecture 10: UFD continued

Lecture 11: UFD continued

Chapter 7: Section 7: Chapter 7- Distribution of Sample Means and Proportions

Lecture 1: Probability distribution of means of random samples from a population

Lecture 2: Example of computing the probabilities of sample means

Lecture 3: Example of computing the probabilities of sample means

Lecture 4: Example of computing the probabilities of sample means

Lecture 5: Distribution of sample proportions

Lecture 6: Example of computing the probabilities of sample proportions

Lecture 7: Example of computing the probabilities of sample proportions

Lecture 8: Example of computing the probabilities of sample proportions

Chapter 8: Section 8: Chapter 8- Confidence Intervals for Population Means and Proportions

Lecture 1: Confidence interval of population mean and proportion

Lecture 2: Example of confidence interval of mean (CI)

Lecture 3: Example of (CI) of population mean

Lecture 4: Example of CI of population mean

Lecture 5: Example of CI of population proportion

Lecture 6: CI of population proportion and margin of error

Chapter 9: Section 9: Chapter 9- Testing Hypotheses For Population Means and Proportions

Lecture 1: Hypothesis testing for population mean and proportion

Lecture 2: Example of two-tailed hypothesis testing for population mean

Lecture 3: Example of two-tailed hypothesis testing for population mean

Lecture 4: Example of two-tailed hypothesis testing for population mean

Lecture 5: Example of two-tailed hypothesis testing for population mean

Lecture 6: Example of two-tailed hypothesis testing for population mean

Lecture 7: Example of two-tailed hypothesis testing for population mean

Lecture 8: 1-tailed hypothesis testing of population mean and proportion

Lecture 9: Example of 1-tailed hypothesis testing of population mean (mu)

Lecture 10: 1-tailed hypothesis testing of population proportion (pie)

Lecture 11: 1-tailed hypothesis testing of population proportion

Chapter 10: Section 10: Chapter 10- Testing Equality of Two means, Proportions, Variances

Lecture 1: Testing two population means, proportions and variances for equality

Lecture 2: Testing equality of two population means, example

Lecture 3: Testing equality of two population means, example

Instructors

Bahram Adrangi, PhD
Learning Statistics Hands-On Using MS EXCL

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