####################################################################################### Chapter 2 key info
## [--Z Score--]: Value - Mean / Standard deviation.
## [--Density Curve--]: Overall pattern of the distribution, Total area sums to one.
## [--Sigma--]: σ “sigma” is the standard deviation of the density curve.
## [--Mu--]: μ “mu” is the mean of the density curve.
## [--Normal Distribution--]: Symmetric, unimodal, and bell shaped 1SD 68%, 2SD 95%,
## 3SD 99.7%.
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## Chapter 3 key info
## [--Response Variable--]: Measures the outcome or the study, also called the
## dependent variable or "y"
## [--Explanatory Variable--]: Helps explain or influence the changes in the response
## variable. Also refereed to independent variable or "X"
## [--Scatter Plot--]: A graph that displays the relationship between X and Y from a
## single individual. Label X and Y axis intervals need to be equal scale can be
## different. Key parts {Ex img:imagebin.ca/v/3LxEZ1lTRw24}. How to describe a
## scatter plot {there is a "strength", "direction", "form" relationship between
## "explanatory" and "responses"}.
## [--Correlation--]: A measure of direction and strength of a linear relationship
## between two quantitative variables written as "r"
## [--r--]: "r" is always a number between -1 and 1. A positive association if "r"
## is positive and negative association if "r" is negative. A perfect linear
## relationship if -1 or 1.
## [--Regression Line--]: A straight line that describes how a response variable
## "y" changes as an explanatory variable "x" changes. Requires specific explanatory
## and response variables. Can be used to make predictions.
## [--Least squares regression line--]: the line that makes the sum of the squared
## vertical distances of actual data points from the predicted line as small as
## as possible. Formula: [-- p̂ = a + bx --] A=y-intercept b=slope y-hat is the
## predicted y. on formula sheet its p̂ = b0 + (b1 X).
## [--Predicted--]: Determine a specific y value from a given x value.
## [--Extrapolation--]: A prediction make that is outside of the domain of the
## explanatory variable.
## [--Describe an association--]: imagebin.ca/v/3LxRMgZrkFjp
## [--Fit of regression line--]: r=correlation measures strength and direction,
## residual=(observed value of y - predicted value of y) residual plot: a scatter
## plot of the explanatory variables and the residuals, R^2 percent of the variation
## in y can be attributed to the least squares linear relationship between x and y.
## [--Residual model--]: {Ex img: imagebin.ca/v/3LxTZRAe7xwO}
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## Chapter 4 key info
## [--Causation--]: {Ex img: imagebin.ca/v/3LxWisyfKcaQ}
## {Ex img: imagebin.ca/v/3LxXKfElttz4}
## [--Linear Growth--]: Increase by a fixed amount in each equal time period
## form: y=a +bx.
## [--Exponential Growth--]: Increased by a fixed percent of the previous total
## form: Y=ab^x
## [--Power Model--]: Increases by a constant power form: Y=ax^b
##
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