QBA 260 Chapter 1

Chapter 1 Topics Samples and Populations Samples and Populations Types of Data Types of Data Variables – Independent and Dependent Variables – Independent and Dependent Probability Probability Hypothesis Testing Hypothesis Testing Types of Error Types of Error Intro to Excel Intro to Excel

Samples and Populations Why do we sample? Why do we sample? Examples of sampling Examples of sampling What does “inferential statistics” mean? What does “inferential statistics” mean? Terms: Terms: Population – Parameters Population – Parameters Sample - Statistics Sample - Statistics

Types of Data Nominal Nominal Ordinal Ordinal Interval Interval Ratio Ratio

Variables Variable = something that can take on more than one value Variable = something that can take on more than one value Independent → Dependent Independent → Dependent Examples Examples

Probability Probability = the chance of something happening Probability = the chance of something happening Probability = (number of ways the event can occur)/(total number of possible events) Probability = (number of ways the event can occur)/(total number of possible events) What is the probability of getting a “head” if you flip a coin? What is the probability of getting a “head” if you flip a coin? What is the probability of getting 2 fours if you roll two dice? What is the probability of getting 2 fours if you roll two dice? Conditional Probability – the chance of something happening given some condition Conditional Probability – the chance of something happening given some condition

Hypothesis Testing Null Hypothesis (H 0 ) – our machine is working correctly Null Hypothesis (H 0 ) – our machine is working correctly Alternative Hypothesis (H 1 ) – our machine is not working correctly Alternative Hypothesis (H 1 ) – our machine is not working correctly Choices: to reject H 0 or not to reject H 0 Choices: to reject H 0 or not to reject H 0

Hypothesis Testing – Another Example Let’s say there are 3 different teaching methods for a particular college course: Let’s say there are 3 different teaching methods for a particular college course: Full in-class Full in-class Full on-line Full on-line Combination of in-class and on-line Combination of in-class and on-line Null Hypothesis (H 0 ) – Full in-class teaching is the best teaching method Null Hypothesis (H 0 ) – Full in-class teaching is the best teaching method Alternative Hypothesis (H 1 ) – Full in-class teaching is not the best teaching method Alternative Hypothesis (H 1 ) – Full in-class teaching is not the best teaching method Choices: If you reject H 0 then you know that full in-class teaching is not the best method but you still do not know which of the other two methods is better Choices: If you reject H 0 then you know that full in-class teaching is not the best method but you still do not know which of the other two methods is better

Two types of Error Type 1 Error – when you reject H 0 and you should not have Type 1 Error – when you reject H 0 and you should not have (example: you rejected the hypothesis that the machine was working correctly and brought in the repair crew to fix it; however, the machine was working correctly to begin with) (example: you rejected the hypothesis that the machine was working correctly and brought in the repair crew to fix it; however, the machine was working correctly to begin with) Type II Error – when you do not reject H 0 and you should have Type II Error – when you do not reject H 0 and you should have (example: you did not reject the null hypothesis and assumed the machine was working correctly when it really was not) (example: you did not reject the null hypothesis and assumed the machine was working correctly when it really was not)

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