1. 26134 Business Statistics Mahrita.Harahap@uts.edu.au Maths Study Centre CB04.03.331 Tutorial 8: Probability Distribution Key concepts in this tutorial are listed below 1.REVISION 2.Random Variables 3.Identifying Normal Distribution 4.Normal Distribution: Z-Score, Converting non-standard normal distributions into standard normal distributions, Calculating Probabilities using normal distribution applying the complement rule and/or symmetry rule and/or interval rule 5.Applications of Normal Distribution in calculating probabilities. 1

2. 2 The topics to be tested in Quiz 2 are: THRESHOLD 3: Relating variables and analyzing relationships between variables. The specific topics for this threshold are: 1.) Lecture Topic: Simple Linear Regression 2.) Lecture Topic: Multiple Linear Regression 3.) Lecture Topic: Issues with Regression THRESHOLD 4: Theoretical foundation of statistical inference- Understanding events and using data to calculate the probability of occurrence of an event. The specific topics for this threshold are: 1.) Lecture Topic: Probability The sample quiz will be uploaded one week prior to the quiz.

3. REVISION: Probability To find certain probabilities, first construct a contingency table (frequency table/remember to add TOTAL columns and rows), then construct a probability table (relative frequency table): Marginal Probability (row or column total probability) P(A) Intersection Event/Joint Event (A AND B) P(∩) Union Event (A OR B) P(A U B) = P(A) + P(B) – P(A ∩ B) Complement Event (NOT A) P(A’)=1−(A) Mutual Exclusive Events (cannot occur at the same time) P(∩)=0 Conditional Probability (A GIVEN B) P(A|B)= P(A ∩ B)/P(B) Independent Events P(∩)=P(A)*P(B) 3

4. REVISION: Regression Regression Equation Interpretation of coefficient: As “Predictor1” increases by 1 “unit” then “Response” increases/decreases by β 1 ”units”, holding other variables as constant. Significance of variables: Use t-test Significance of regression model: Use F-test The adjusted R 2 recalculates the R 2 based on the number of independent variables in the model and the sample size. Calculates the model performance. To predict the response based on given values of predictors, substitute those values into the regression equation to find the predicted value. 4

5. Random Variables and Normal Distribution A random variable X is defined as a unique numerical value associated with every outcome of an experiment. If X follows a normal distribution, then it is denoted as X~N(μ,σ) To find probabilities under the normal distribution, random variable X must be converted to random variable Z that follows a standard normal distribution denoted as Z~N(μ=0,σ=1). We need to do this to standardise the distribution. To convert random variable X to random variable Z, we calculate the z-score=(x- μ)/ σ 5

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7. Calculating Probabilities using normal distribution applying the complement rule and/or symmetry rule and/or interval rule Complement Rule P(Z>z)=1-P(Z<z) Symmetry Rule P(Z z) Interval Rule P(-z<Z<z)=P(Z<z)-P(Z<-z) 7

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