Business Statistics for Beginners
uCertify BUS-STATS.AE1
Business Statistics for Beginners
आईएसबीएन: 978-1-64459-461-2uCertify BUS-STATS.AE1
Business Statistics for Beginners
(BUS-STATS.AE1) / आईएसबीएन : 978-1-64459-461-2
इस पाठ्यक्रम में शामिल हैं
Lessons
Lab
इस कोर्स के बारे में
शुरुआती लोगों के लिए व्यावसायिक सांख्यिकी पाठ्यक्रम सांख्यिकीय विश्लेषण के लिए नए और व्यावसायिक क्षेत्र में डेटा की क्षमता को अनलॉक करने के लिए उत्सुक व्यक्तियों को सशक्त बनाने के लिए डिज़ाइन किया गया है। इस कोर्स में, हमने आपको व्यावसायिक सेटिंग में सांख्यिकीय अंतर्दृष्टि का प्रभावी ढंग से उपयोग करने के लिए आवश्यक बुनियादी कौशल सीखने में मदद करने के लिए सामग्री को सावधानीपूर्वक व्यवस्थित किया है। पाठ्यक्रम उन प्रमुख क्षेत्रों को कवर करता है जो व्यावसायिक सांख्यिकी की रीढ़ बनाते हैं। मौलिक अवधारणाओं से लेकर व्यावहारिक अभ्यास और वास्तविक दुनिया के अनुप्रयोगों तक, यह पाठ्यक्रम आपको कच्चे डेटा को कार्रवाई योग्य अंतर्दृष्टि में बदलने के लिए उपकरणों से लैस करता है।
कौशल जो आपको प्राप्त होंगे
आपको जिस सहायता की आवश्यकता है उसे प्राप्त करें। हमारे प्रशिक्षक-नेतृत्व वाले पाठ्यक्रम में नामांकन करें।
पाठ
20+ पाठ | 55+ अभ्यास | 276+ प्रश्नोत्तरी | 100+ फ़्लैशकार्ड | 100+ पारिभाषिक शब्दावली
1
Introduction
- About This Course
- Foolish Assumptions
- Icons Used in This Course
- Where to Go from Here
2
The Art and Science of Business Statistics
- Representing the Key Properties of Data
- Probability: The Foundation of All Statistical Analysis
- Using Sampling Techniques and Sampling Distributions
- Statistical Inference: Drawing Conclusions from Data
3
Pictures Tell the Story: Graphical Representations of Data
- Analyzing the Distribution of Data by Class or Category
- Histograms: Getting a Picture of Frequency Distributions
- Checking Out Other Useful Graphs
4
Finding a Happy Medium: Identifying the Center of a Data Set
- Looking at Methods for Finding the Mean
- Getting to the Middle of Things: The Median of a Data Set
- Comparing the Mean and Median
- Discovering the Mode: The Most Frequently Repeated Element
5
Searching High and Low: Measuring Variation in a Data Set
- Determining Variance and Standard Deviation
- Finding the Relative Position of Data
- Measuring Relative Variation
6
Measuring How Data Sets Are Related to Each Other
- Understanding Covariance and Correlation
- Interpreting the Correlation Coefficient
7
Probability Theory: Measuring the Likelihood of Events
- Working with Sets
- Betting on Uncertain Outcomes
- Looking at Types of Probabilities
- Following the Rules: Computing Probabilities
8
Probability Distributions and Random Variables
- Defining the Role of the Random Variable
- Assigning Probabilities to a Random Variable
- Characterizing a Probability Distribution with Moments
9
The Binomial, Geometric, and Poisson Distributions
- Looking at Two Possibilities with the Binomial Distribution
- Determining the Probability of the Outcome That Occurs First: Geometric Distribution
- Keeping the Time: The Poisson Distribution
10
The Uniform and Normal Distributions: So Many Possibilities!
- Comparing Discrete and Continuous Distributions
- Working with the Uniform Distribution
- Understanding the Normal Distribution
11
Sampling Techniques and Distributions
- Sampling Techniques: Choosing Data from a Population
- Sampling Distributions
- The Central Limit Theorem
12
Confidence Intervals and the Student’s t-Distribution
- Almost Normal: The Student’s t-Distribution
13
Testing Hypotheses about the Population Mean
- Applying the Key Steps in Hypothesis Testing for a Single Population Mean
14
Testing Hypotheses about Multiple Population Means
- Getting to Know the F-Distribution
- Using ANOVA to Test Hypotheses
15
Testing Hypotheses about the Population Mean
- Staying Positive with the Chi-Square Distribution
- Testing Hypotheses about the Population Variance
- Practicing the Goodness of Fit Tests
- Testing Hypotheses about the Equality of Two Population Variances
16
Simple Regression Analysis
- The Fundamental Assumption: Variables Have a Linear Relationship
- Defining the Population Regression Equation
- Estimating the Population Regression Equation
- Testing the Estimated Regression Equation
- Using Statistical Software
- Assumptions of Simple Linear Regression
17
Multiple Regression Analysis: Two or More Independent Variables
- The Fundamental Assumption: Variables Have a Linear Relationship
- Estimating a Multiple Regression Equation
- Checking for Multicollinearity
18
Forecasting Techniques: Looking into the Future
- Defining a Time Series
- Modeling a Time Series with Regression Analysis
- Forecasting a Time Series
- Changing with the Seasons: Seasonal Variation
- Implementing Smoothing Techniques
- Comparing the Forecasts of Different Models
19
Ten Common Errors That Arise in Statistical Analysis
- Designing Misleading Graphs
- Drawing the Wrong Conclusion from a Confidence Interval
- Misinterpreting the Results of a Hypothesis Test
- Placing Too Much Confidence in the Coefficient of Determination (R2)
- Assuming Normality
- Thinking Correlation Implies Causality
- Drawing Conclusions from a Regression Equation when the Data do not Follow the Assumptions
- Including Correlated Variables in a Multiple Regression Equation
- Placing Too Much Confidence in Forecasts
- Using the Wrong Distribution
20
Ten Key Categories of Formulas for Business Statistics
- Summary Measures of a Population or a Sample
- Probability
- Discrete Probability Distributions
- Continuous Probability Distributions
- Sampling Distributions
- Confidence Intervals for the Population Mean
- Testing Hypotheses about Population Means
- Testing Hypotheses about Population Variances
- Using Regression Analysis
- Forecasting Techniques
The Art and Science of Business Statistics
- Understanding the Daily Step Counts of Your Club Members
- Keeping Track of Visitors on a Personal Blog
- Assessing the Level of Student Participation in Various Extracurricular Activities
- Conducting a Survey
- Visualizing the Temperature Fluctuations
- Visualizing Exam Grades Distribution
Pictures Tell the Story: Graphical Representations of Data
- Calculating the Relative Frequency
- Figuring the Class Width
- Calculating the Cumulative Frequency
- Illustrating a Cumulative Frequency
- Illustrating a Relative Frequency
- Illustrating a Frequency Distribution
- Representing Fluctuations of Gold Price
Finding a Happy Medium: Identifying the Center of a Data Set
- Calculating the Arithmetic Mean
- Calculating the Weighted Geometric Mean
- Calculating the Weighted Arithmetic Mean
- Representing Positively Skewed Data Set
- Representing Negatively Skewed Data Set
- Representing Symmetrical Data Set
- Discovering the Mode
Searching High and Low: Measuring Variation in a Data Set
- Calculating Percentiles
- Finding Quartiles
- Finding Coefficient of Variation
Measuring How Data Sets Are Related to Each Other
- Calculating the Sample Covariance
Probability Theory: Measuring the Likelihood of Events
- Performing Set Operations
- Looking at Types of Probabilities
- Finding Unconditional Probabilities
- Finding the Conditional Probability
- Calculating the Multiplication Rule
- Calculating the Complement Rule
Probability Distributions and Random Variables
- Calculating the Probability Distribution
- Calculating the Expected Value
The Binomial, Geometric, and Poisson Distributions
- Calculating the Binomial Probability
- Representing the Binomial Distribution
- Calculating Geometric Probabilities
- Computing Poisson Probabilities
The Uniform and Normal Distributions: So Many Possibilities!
- Representing the Discrete Distribution
- Uniform Distribution: Computing Variance and Standard Deviation
- Calculating the Expected Value
- Computing Uniform Probabilities with Formulas
Sampling Techniques and Distributions
- Portraying Sampling Distributions Graphically
- Calculating the Moments a Sampling Distribution
- Converting Random Variable into a Standard Normal Random Variable
Confidence Intervals and the Student’s t-Distribution
- Graphing the t-distribution
- Calculating the Variance of a t-distribution
Testing Hypotheses about the Population Mean
- Graphing the Standard Normal Distribution
- Determining the Two-Tailed Hypothesis Test
- Determining the Test Statistic
Testing Hypotheses about Multiple Population Means
- Calculating the Error Sum of Squares (SSE)
Testing Hypotheses about the Population Mean
- Testing Hypotheses about the Population Variance
Simple Regression Analysis
- Calculating the Slope of a Line from Two Given Points
- Calculating Coefficients and Predicting Sales Revenue in Simple Linear Regression
- Calculating Total Sum of Squares (TSS)
Multiple Regression Analysis: Two or More Independent Variables
- Visualizing the Test Statistics
Forecasting Techniques: Looking into the Future
- Analyzing User Growth Trends