Chapter 4 Numerical Data and Chapter 5 Probability
Chapter 4 Numerical Data and Chapter 5 Probability Stat 152
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This 2 page Class Notes was uploaded by Samantha Kessler on Saturday October 15, 2016. The Class Notes belongs to Stat 152 at Truckee Meadows Community College taught by Anne Flesher in Fall 2016. Since its upload, it has received 17 views. For similar materials see Introduction to Statistics in Math at Truckee Meadows Community College.
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Date Created: 10/15/16
Chapter 4 and 5 notes The least line of linear regression is the line that minimizes the sum of squared deviations. (can find this by putting in to your calculator) y=a+bx b= the slope, which is the amount that y increases when x decreases by 1 unit. a= the intercept, which is the height of the line about x=0 Calculating slop and interpreting slope> a good line= data points close to the line *deviations will be small in size *above the line deviations are positive *below the line data points are negative Coefficient of determinations (r^2)= the amount of variability in y that can be a characteristic to an approximate linear relationship between x and y. Correlation Coefficient(r) = measures strength and direction of a linear relationship between two number values. [By using the linear regression function in calculator it will find all the stated above points] Probability is denoted as P(outcome) or P(E), where E= event 1P(outcome)= the complement of outcome Law of Large Numbers: As the number of observations increases the amount of time that an outcome occurs, it gets close to the probability of the outcome. * Chance Experiment: the process of making observations when the outcome is uncertain about two or more possible outcomes. * Sample Space: collection of all the possible outcomes of a chance experiment. P(E)=( # of outcomes favorable to E)/(# of outcomes in a sample space) Finding the complement: P(Ec)= 1P(E) where P(Ec) is the probability that E will not occur.
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