ENGR 371

Concordia

Course Overview

Lessons & Practice

I. Welcome

1. Introduction

1.1hr

1.1.1. Introduction to Statistics4 min

1.1.2. Sample vs. Population7 min

1.1.3. Practice

1.1.4. Measurement Scales7 min

1.1.5. Practice

1.2.1. Statistical Sampling28 sec

1.2.2. Convenience Sampling1 min

1.2.3. Simple Random Sampling3 min

1.2.4. Systematic Sampling2 min

1.2.5. Stratified Sampling vs. Cluster Sampling6 min

1.2.6. Two-Stage Cluster Sampling2 min

1.2.7. Practice

1.2.8. Practice

1.2.9. Practice

1.3.1. Sampling Bias7 min

1.3.2. Recipe for a Good Sample5 min

1.3.3. Practice

1.3.4. Practice

1.4.1. Observational Studies4 min

1.4.2. Experimental Studies3 min

1.4.3. Example5 min

1.4.4. Practice

1.5.1. Types of Experimental Designs7 min

1.5.2. Principles of Experimental Design4 min

1.5.4. Practice

1.5.5. Practice

2. Probability

2.4hr

2.1.1. Events & Sample Spaces6 min

2.1.2. Types of Probability6 min

2.1.3. Probability Language & Notation4 min

2.1.4. Example3 min

2.1.5. Independent vs. Mutually Exclusive Events4 min

2.1.6. Example2 min

2.1.7. Practice

2.2.1. Venn Diagrams3 min

2.2.2. Tree Diagrams4 min

2.2.3. Two-way Table5 min

2.3.1. Interpretation of Probability2 min

2.3.2. Complement Rule3 min

2.3.3. Practice1 min

2.3.4. The Addition Rule4 min

2.4.1. Probability of Independent Events4 min

2.4.2. Example3 min

2.4.3. Practice2 min

2.5.1. Probability of Dependent Events (Conditional Probability)7 min

2.5.2. Conditional Probability: Tree Diagram5 min

2.5.3. Bayes’ Theorem4 min

2.5.4. Example4 min

2.5.5. Practice3 min

2.6.1. Example6 min

2.6.2. Example7 min

2.6.3. Example7 min

2.6.4. Practice4 min

2.6.5. Practice5 min

2.7.1. Summary of Key Definitions6 min

2.8.1. Fundamental Counting Principle6 min

2.8.2. Permutations and Combinations6 min

2.8.3. Practice1 min

2.8.4. Practice54 sec

2.8.5. More Combination Rules3 min

2.8.6. Example4 min

2.8.7. Practice3 min

2.8.8. Practice

2.9.1. Counting and Probability3 min

2.9.3. Practice1 min

3. Discrete Random Variables

3hr

3.1.1. Discrete Random Variables8 min

3.1.2. Practice

3.1.3. Probability Distribution Function4 min

3.1.4. Expected Value, Variance, and Standard Deviation of Discrete Random Variables6 min

3.1.5. Example5 min

3.1.6. Example4 min

3.1.7. Practice6 min

3.1.8. Practice4 min

3.1.9. Practice

3.2.1. Transforming Data: Shifting & Scaling11 min

3.2.2. Practice2 min

3.2.3. Linear Transformation3 min

3.2.4. Example4 min

3.2.5. Practice2 min

3.3.1. Cumulative Distribution Function (CDF)3 min

3.3.2. Example6 min

3.3.3. Practice2 min

3.4.1. Combining Random Variables7 min

3.4.2. Example2 min

3.4.3. Practice2 min

3.4.4. Example3 min

3.4.5. Practice

3.4.6. Functions of Two Independent Random Variables X and Y5 min

3.4.7. Example3 min

3.4.8. Practice

3.4.9. Practice2 min

3.5.1. Bernoulli Trials4 min

3.5.2. Practice4 min

3.5.3. Binomial Experiment4 min

3.5.4. Binomial Distribution8 min

3.5.5. Example10 min

3.5.6. Example1 min

3.5.7. Practice6 min

3.5.8. Practice

3.6.1. Poisson Experiment5 min

3.6.2. Poisson Distribution1 min

3.6.3. Example3 min

3.6.4. Practice5 min

3.6.5. Practice5 min

3.7.1. Approximating the Binomial Distribution with Poisson2 min

3.7.2. Example6 min

3.7.3. Practice

3.8.1. Hypergeometric Experiment5 min

3.8.2. Hypergeometric Distribution9 min

3.8.3. Example4 min

3.8.4. Practice4 min

3.8.5. Practice Question

4. Continuous Probability Distributions

1.6hr

4.1.1. Continuous Probability Distribution Theory10 min

4.2.1. Uniform Distribution1 min

4.2.2. Practice3 min

4.3.1. Exponential Experiment3 min

4.3.2. Exponential Distribution7 min

4.3.3. Practice7 min

4.4.1. Normal Distribution11 min

4.4.2. Practice5 min

4.5.1. Standard Normal Distribution (Z-Scores)11 min

4.5.2. Standardizing a Normal Random Variable4 min

4.5.3. Example10 min

4.5.4. Practice6 min

4.5.5. Practice3 min

4.6.1. Normal Approximation to Binomial2 min

4.6.2. Practice5 min

4.6.3. Practice4 min

4.6.4. Practice4 min

5. Joint Probability Distributions

23min

5.1.1. Conditional Probability & Joint Distribution7 min

5.1.2. Example5 min

5.1.3. Practice

5.1.4. Random Variables & Joint Distribution7 min

5.1.5. Example3 min

5.1.6. Practice

5.1.8. Practice

6. Descriptive Statistics

1.4hr

6.1.1. Measures of Central Tendency7 min

6.1.2. Mean and Median Example2 min

6.1.3. Practice

6.1.4. Practice2 min

6.1.5. Practice3 min

6.2.1. Percentiles4 min

6.2.2. Example2 min

6.2.3. Practice

6.3.1. Range and Interquartile Range11 min

6.3.2. Variance and Standard Deviation6 min

6.3.3. Practice3 min

6.3.4. Practice3 min

6.3.5. Practice

6.4.1. Skewness and Symmetry 6 min

6.4.2. Practice

6.5.1. Stemplots (Stem-and-Leaf Plots)2 min

6.5.2. Practice3 min

6.6.1. Frequency Polygons2 min

6.6.2. Practice4 min

6.7.1. Histograms4 min

6.7.2. Practice2 min

6.7.3. Practice2 min

6.7.4. Practice

6.8.1. Boxplots and Five Number Summary9 min

6.8.2. Practice4 min

6.8.3. Practice

6.9.1. Times Series Plots2 min

6.9.2. Practice2 min

7. Sampling Distributions and Point Estimation of Parameters

59min

7.1.1. Sampling Variability & Sampling Distribution12 min

7.2.1. Central Limit Theorem 5 min

7.2.2. Sampling Distribution for a Mean2 min

7.2.3. Example9 min

7.2.4. Sampling Distribution for a Mean Question5 min

7.3.1. Sampling Distribution of Proportions5 min

7.3.2. Example3 min

7.3.3. Practice4 min

7.4.1. Drawing Samples From a Finite Population8 min

7.4.2. Example4 min

7.4.3. Practice3 min

8. Statistical Intervals for a Single Sample

2.3hr

8.1.1. Introduction to Confidence Intervals14 min

8.2.1. Confidence Interval for a Mean13 min

8.2.2. Example4 min

8.2.3. Practice3 min

8.3.1. Confidence Interval for a Proportion2 min

8.3.2. Example5 min

8.3.3. Practice4 min

8.4.1. Margin of Error and Sample Size3 min

8.4.2. Practice

8.5.1. Inference for Population Variance & Chi-square Distribution 8 min

8.6.1. Confidence Interval for Population Variance5 min

8.6.2. Example6 min

8.6.3. Pratice3 min

8.6.4. Practice

8.7.1. Estimating the Coefficients of the Linear Regression Model9 min

8.7.2. Measures of Variation in Regression9 min

8.7.3. Practice

8.8.1. Confidence Interval for Linear Regression5 min

8.8.2. Example9 min

8.8.3. Practice3 min

8.8.4. Slope: The Significance of "Zero" (or Lack Thereof)6 min

8.8.5. Practice

8.9.1. Prediction Interval of Y7 min

8.9.2. Example12 min

8.9.3. Practice10 min

8.9.4. Practice

9. Tests of Hypothesis for a Single Sample

3hr

9.1.1. Introduction to Hypothesis Testing 15 min

9.1.2. Hypothesis Testing Steps25 min

9.2.1. Hypothesis Test for a Mean3 min

9.2.2. Example16 min

9.2.3. Example8 min

9.2.4. Practice4 min

9.3.1. Hypothesis Test for a Proportion10 min

9.3.2. Example4 min

9.3.3. Practice5 min

9.3.4. Practice9 min

9.4.1. Type I and Type II Errors5 min

9.4.2. Type I and Type II Errors1 min

9.4.3. Practice

9.4.4. Practice

9.4.5. Power7 min

9.4.6. Practice

9.5.1. Hypothesis Testing for Linear Regression14 min

9.5.2. Example17 min

9.5.3. Example13 min

9.5.4. Practice: Linear Regression Analysis6 min

9.6.1. Hypothesis Test for Population Variance8 min

9.6.2. Example8 min

9.6.3. Hypothesis Test for Population Variance

9.6.4. Practice4 min

Mock Exams

ME.1. Mock Exam21 Qns

ME.2. Mock Exam18 Qns

ME.3. Mock Exam27 Qns

ME.4. Midterm Exam - Mock Assessment24 Qns

ME.5. Final Exam - Mock Assessment30 Qns

ME.3 Mock Exam

medal

Included in Wizeprep Plus

Mock Exam

This mock exam has 27 questions

You must answer all questions before submitting the quiz

Timer

Enabled

mins

Want a tour?