Statistics for Data Analysis Using Excel

Statistics for Data Analysis Using Excel, available at $44.99, has an average rating of 4, with 60 lectures, based on 26 reviews, and has 91 subscribers.

You will learn about Learn all about Descriptive and Inferential Statistics with practical examples Learn to use the power of Microsoft Excel to perform statistical calculations for you Analyze a population using data samples Get an idea about Central Limit Theorem Calculate Co-variance and Co-relation among data After finishing our Course you will be able to calculate the measures of central tendency, asymmetry, and variability Perform hypothesis testing Learn how to work with different types of data This course is ideal for individuals who are Anyone who want to start learning statistics or Anyone who want to learn the fundamentals of statistics or Anyone who want to quickly Understand the leverage of data or Anyone who want to turn data into information It is particularly useful for Anyone who want to start learning statistics or Anyone who want to learn the fundamentals of statistics or Anyone who want to quickly Understand the leverage of data or Anyone who want to turn data into information.

Enroll now: Statistics for Data Analysis Using Excel

Summary

Title: Statistics for Data Analysis Using Excel

Price: $44.99

Average Rating: 4

Number of Lectures: 60

Number of Published Lectures: 60

Number of Curriculum Items: 60

Number of Published Curriculum Objects: 60

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn all about Descriptive and Inferential Statistics with practical examples
Learn to use the power of Microsoft Excel to perform statistical calculations for you
Analyze a population using data samples
Get an idea about Central Limit Theorem
Calculate Co-variance and Co-relation among data
After finishing our Course you will be able to calculate the measures of central tendency, asymmetry, and variability
Perform hypothesis testing
Learn how to work with different types of data

Who Should Attend

Anyone who want to start learning statistics
Anyone who want to learn the fundamentals of statistics
Anyone who want to quickly Understand the leverage of data
Anyone who want to turn data into information

Target Audiences

Anyone who want to start learning statistics
Anyone who want to learn the fundamentals of statistics
Anyone who want to quickly Understand the leverage of data
Anyone who want to turn data into information

Get marketable data analytic skills in this course using Microsoft Excel.

This course is about Statistics and Data Analysis. The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concept. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use Microsoft Excel to perform these calculations.

Many tests covered, including different t tests, ANOVA, post hoc tests, correlation, and regression.

Following areas of statistics are covered:

Descriptive Statistics– Mean, Mode, Median

Variability– Standard Deviation, Variance, Range

Population and Sampling

Probability Distributions

Hypothesis Testing– One Sample and Two Samples – z Test, t Test, p Test, F Test, Chi Square Test

ANOVA – Perform Analysis of Variance (ANOVA) step by step doing manual calculation and by MS Excel.

Learning Objectives:

Explain how to calculate simple probability.
Review the Excel statistical formulas for finding mean, median, and mode.
Differentiate statistical nomenclature when calculating variance.
Identify components when graphing frequency polygons.
Explain how t-distributions operate.
Describe the process of determining a chi-square.

Enroll in this course and obtain important marketable data analytic skills

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Excel Statistics Fundamentals

Lecture 1: Using Excel functions

Lecture 2: Understanding Excel statistics functions

Lecture 3: Working with Excel graphics

Lecture 4: Installing the Excel Analysis Toolpak

Chapter 3: Types of Data

Lecture 1: Differentiating data types

Lecture 2: Independent and dependent variables

Chapter 4: Probability

Lecture 1: Defining probability

Lecture 2: Calculating probability

Lecture 3: Understanding conditional probability

Chapter 5: Central Tendency

Lecture 1: The mean and its properties

Lecture 2: Working with the median

Lecture 3: Working with the mode

Chapter 6: Variability

Lecture 1: Understanding variance

Lecture 2: Understanding standard deviation

Lecture 3: Z-scores

Chapter 7: Distributions

Lecture 1: Organizing and graphing a distribution

Lecture 2: Graphing frequency polygons

Lecture 3: Properties of distributions

Lecture 4: Probability distributions

Chapter 8: Normal Distributions

Lecture 1: The standard normal distribution

Lecture 2: Meeting the normal distribution family

Lecture 3: Standard normal distribution probability

Lecture 4: Visualizing normal distributions

Chapter 9: Sampling Distributions

Lecture 1: Introducing sampling distributions

Lecture 2: Understanding the central limit theorem

Lecture 3: Meeting the t-distribution

Chapter 10: Estimation

Lecture 1: Confidence in estimation

Lecture 2: Calculating confidence intervals

Chapter 11: Hypothesis Testing

Lecture 1: The logic of hypothesis testing

Lecture 2: Type I errors and Type II errors

Chapter 12: Testing Hypotheses about a Mean

Lecture 1: Applying the central limit theorem

Lecture 2: The z-test and the t-test

Chapter 13: Testing Hypotheses about a Variance

Lecture 1: The chi-squared distribution

Chapter 14: Independent Samples Hypothesis Testing

Lecture 1: Understanding independent samples

Lecture 2: Distributions for independent samples

Lecture 3: The z-test for independent samples

Lecture 4: The t-test for independent samples

Chapter 15: Matched Samples Hypothesis Testing

Lecture 1: Understanding matched samples

Lecture 2: Distributions for matched samples

Lecture 3: The t-test for matched samples

Chapter 16: Testing Hypotheses about Two Variances

Lecture 1: Working with the F-test

Chapter 17: The Analysis of Variance

Lecture 1: Testing more than two parameters

Lecture 2: Introducing ANOVA

Lecture 3: Applying ANOVA

Chapter 18: After the Analysis of Variance

Lecture 1: Types of post-ANOVA testing

Lecture 2: Post-ANOVA planned comparisons

Chapter 19: Repeated Measures Analysis

Lecture 1: What is repeated measures?

Lecture 2: Applying repeated measures ANOVA

Chapter 20: Hypothesis Testing with Two Factors

Lecture 1: Statistical interactions

Lecture 2: Two-factor ANOVA

Lecture 3: Performing two-factor ANOVA

Chapter 21: Regression

Lecture 1: Understanding the regression line

Lecture 2: Variation around the regression line

Lecture 3: Analysis of variance for regression

Lecture 4: Multiple regression analysis

Chapter 22: Correlation

Lecture 1: Hypothesis testing with correlation

Lecture 2: Understanding correlation

Lecture 3: The correlation coefficient

Lecture 4: Correlation and regression

Instructors

Data Lab
Creating opportunities for Data Science and Business Strateg

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 5 votes
4 stars: 11 votes
5 stars: 8 votes

Frequently Asked Questions

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