Residuals and outliers

... pattern. Durbin-Watson test can be used to test for time effect. The Durbin-Watson statistic can be gotten in SPSS via Regression → Linear → Statistics → DurbinWatson. Values of the statistic larger than 2.5 or less than 1.5 are indicative of a time effect. Outliers As in simple regression, outliers ...

... pattern. Durbin-Watson test can be used to test for time effect. The Durbin-Watson statistic can be gotten in SPSS via Regression → Linear → Statistics → DurbinWatson. Values of the statistic larger than 2.5 or less than 1.5 are indicative of a time effect. Outliers As in simple regression, outliers ...

Formulas needed for EOCT Arithmetic Sequences Geometric

... To find the MAD find the mean of the third column. If you have a TI-36X Pro you can make this table on your calculator under L1, L2, and L3 and enter a formula under L3. Finding linear and exponential models of best fit (regression): Enter all of the x data under L1 and the y data under L2 in your c ...

... To find the MAD find the mean of the third column. If you have a TI-36X Pro you can make this table on your calculator under L1, L2, and L3 and enter a formula under L3. Finding linear and exponential models of best fit (regression): Enter all of the x data under L1 and the y data under L2 in your c ...

Exam I Review Math 1530: Elements of Statistics Type of variables

... 7. When data contains extreme observations (outliers), IQR can be a good measure of spread. 8. A z-score tells the direction and number of means a data value is from the standard deviation. 9. The median lies to the right of the mean for a right-skewed distribution. 10. The regression line has the p ...

... 7. When data contains extreme observations (outliers), IQR can be a good measure of spread. 8. A z-score tells the direction and number of means a data value is from the standard deviation. 9. The median lies to the right of the mean for a right-skewed distribution. 10. The regression line has the p ...

L20: Outliers

... already have a good model M , then why do we can about outliers? So here we discuss properties of techniques that build a model M and are resistant, or robust, to outliers. Given a model M (P ), its breakdown point is an upper bound of the fraction of points in P that can be moved to ∞ and for M (P ...

... already have a good model M , then why do we can about outliers? So here we discuss properties of techniques that build a model M and are resistant, or robust, to outliers. Given a model M (P ), its breakdown point is an upper bound of the fraction of points in P that can be moved to ∞ and for M (P ...

Week2

... • The statistical model corresponds to the information a statistician brings to the application about what the true distribution is or at least what he or she is willing to assume about it. • The variable θ is called the parameter of the model, and the set Ω is called the parameter space. ...

... • The statistical model corresponds to the information a statistician brings to the application about what the true distribution is or at least what he or she is willing to assume about it. • The variable θ is called the parameter of the model, and the set Ω is called the parameter space. ...

Bivariate Data Cleaning

... Applying group-specific outlier analysis • Do outlier analyses and trimming/Windsorizing separately for each group • This gets lengthy when working with factorial designs -- remember the purpose of each condition!!! • Some suggest taking an analogous approach with doing regression analyses that invo ...

... Applying group-specific outlier analysis • Do outlier analyses and trimming/Windsorizing separately for each group • This gets lengthy when working with factorial designs -- remember the purpose of each condition!!! • Some suggest taking an analogous approach with doing regression analyses that invo ...

Maths-S1

... Use frequency density/area to construct a histogram Read information from a histogram and use it to calculate frequencies Construct and/or interpret a scatter diagram Construct and/or interpret a stem and leaf diagram (including back to back) Construct and/or interpret a box (and whisker)plot includ ...

... Use frequency density/area to construct a histogram Read information from a histogram and use it to calculate frequencies Construct and/or interpret a scatter diagram Construct and/or interpret a stem and leaf diagram (including back to back) Construct and/or interpret a box (and whisker)plot includ ...

point estimate

... Some General Concepts of Point Estimation A sensible way to quantify the idea of being close to is to consider the squared error ( )2. For some samples, will be quite close to and the resulting squared error will be near 0. Other samples may give values of far from , corresponding to very larg ...

... Some General Concepts of Point Estimation A sensible way to quantify the idea of being close to is to consider the squared error ( )2. For some samples, will be quite close to and the resulting squared error will be near 0. Other samples may give values of far from , corresponding to very larg ...

Exploratory Data Analysis

... with each value and its frequency of occurrence. You can also compute a checking variable using the COMPUTE command. Create a new variable that gives a 1 if a value is between minimum and maximum, and a 0 if the value is outside that range. Best way to examine categorical variables is by ...

... with each value and its frequency of occurrence. You can also compute a checking variable using the COMPUTE command. Create a new variable that gives a 1 if a value is between minimum and maximum, and a 0 if the value is outside that range. Best way to examine categorical variables is by ...

Assessing the Uncertainty of Point Estimates We notice that, in

... bias uncertainty cannot be simply reduced by increasing sample sizes. As illustrated before, naive increases of sample sizes could make a bad situation even worse! An obvious way for reducing bias uncertainty is to increase data quality, but this is usually very expensive when at all possible. An al ...

... bias uncertainty cannot be simply reduced by increasing sample sizes. As illustrated before, naive increases of sample sizes could make a bad situation even worse! An obvious way for reducing bias uncertainty is to increase data quality, but this is usually very expensive when at all possible. An al ...