regression

Question 1

A scatter plot

Answer 1

uses Cartesian coordinates to display the values for two variables so we can visualise the
relationship between them

Question 2

Correlation

Answer 2

measures the relationship between these two continuous variables.

Question 3

The Pearson correlation coefficient

Answer 3

describes the strength of (linear) association between them.

Question 4

Hypothesis test for the correlation between two samples

Answer 4

using Pearson’s Correlation
Coefficient

Question 5

correlation coefficient always takes on a value between -1 and 1 where:

Answer 5

-1: Perfectly negative linear correlation between two variables.
0: No linear correlation between two variables.
1: Perfectly positive linear correlation between two variables.

Question 6

how to determine if a correlation coefficient is statistically significant,

Answer 6

you can calculate the corresponding t-score and p-value.

Question 7

##
## data: Var_1 and Var_2
## t = 7.6064, df = 2, p-value = 0.01685
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4004041 0.9996629
## sample estimates:
## cor
## 0.9831516
How can we interpret the R output?

Answer 7

Since the correlation coefficient is postitve, it indicates that there is a postitve linear relationship between the two variables.

The p-value of the correlation coefficient is less than 0.05, the correlation is statistically significant.

Question 8

difference between Pearson’s correlation coefficient and Spearman’s rank correlation

Answer 8

The Pearson’s correlation coefficient assumes normality for the two samples. However, Spearman’s rank correlation does not (as it is non-parametric).

Question 9

what data is Spearman’s rank correlation appropriate for

Answer 9

both continuous and discrete variables.

Question 10

Spearman’s rank correlation, rather than assuming a linear relationship, measures

Answer 10

the strength of a monotone relationship (i.e. the extent to which if one variable increases, the other one systematically increases/decreases

Question 11

when is a regression analysis used

Answer 11

to try to predict the value of a dependent variable from one or more independent variables,

Question 12

linear model

Answer 12

simplest form of regression model
the response (or dependent) variable is y and the continuous explanatory (or independent) variable
is x.

Question 13

What can we
observed from the graph?

Answer 13

modeling the relationship between these two variables (which we will now call ‘fitting a model’), we
can use this model to predict the value of the dependent variable

Question 14

Linear regression makes a series of assumptions

Answer 14

observations are independent
The residuals should not be predictable from the fitted values in any way.
If any feature of the residuals can be predicted once the fitted value is known, this assumption is violated.

Question 15

## lm(formula = tannin_data$growth ~ tannin_data$tannin)
##

Coefficients:
## (Intercept) tannin_data$tannin
## 11.756 -1.217

how is this interpreted

Answer 15

β0=11.756 (intercept) and β1=-1.217 (slope)

Question 16

Checking the assumptions of the model in R

Answer 16

plot the model
For the first plot what we do not want is for the points to be more scattered as the fitted values increase.
For the second plot the points need to be as close to the line as possible to confirm that the residuals are
normally distributed.

Question 17

what test to compare model fit

Answer 17

F-test can be used to compare a model with no predictors (i.e. intercept-only model)

Question 18

what hypotheses for the F-test are

Answer 18

H0 : The fit of the intercept-only model and your model are equal.
H1 : The fit of the intercept-only model is significantly reduced compared to your model.

Question 19

we say ‘the fit’ we mean;

Answer 19

how well the model fits the data i.e. if an intercept-only data explains the data or if our model that includes the slope explain the data better.

Question 20

how does the f-test determine model fit

Answer 20

Use the quantiles of the F-distribution (qf funcion in R) to find the critical value.
If F-ratio (i.e. SSR/s2) is larger than the critical value we can reject the null hypothesis. To confirm that,
we can obtain the p-value using the pf function (as in Workshop 3) and compare it with the significance
level that we chose.

Question 21

Model uncertainty

Answer 21

When we conduct a regresssion model and estimate β0 and β1 there is uncertainty around the coefficients
we have estimated. This uncertainty can be shown by calculating the standard error of the intercept and
slope The bigger the standard error, the less certain we are about our estimate.

Question 22

Coefficient of determination

Answer 22

denoted r2 is a measure of how well the model fits the data and is the proportion of the variance in the dependent variable that is predictable from the independent variable(s).

Question 23

what does the value of R2 mean

Answer 23

R2 can take values from 0 to 1 (both included), with 0 meaning that the model explains no variation at
all (i.e. the model does not fit the data) and 1 when it explains all the variation. Suppose R2 = 0.82, This
implies that 82% of the variability in the dependent variable has been accounted for in our model and the
remaining 18% is unaccounted for. In reality our models will never perfectly fit the data and it is unrealistic
to expect a value of 1.

Question 24

Finding the t-value and p-value for each coefficient (Hypothesis testing)

Answer 24

Every time we fit a regression model we perform a hypothesis testing for each of the regression parameters.

Question 25

finding the t-value and p-value for linear model

Answer 25

two hypothesis tests: one for the intercept and one for the slope. H0 : coefficient = 0 H1 : coefficient ̸= 0. The t-value for the intercept is calculated by dividing the estimate of the intercept with the standard error of the intercept. The t-value of the slope is calculated in the same way. To find the p-value, since this is a two-tail test If the p-value is less than the significance level, we can reject the null hypothesis and say that the slope is not zero.

Question 26

summary(model) Call: lm(formula = y ~ x, data = df) Residuals: Min 1Q Median 3Q Max -4.4793 -0.9772 -0.4772 1.4388 4.6328 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 11.1432 1.9104 5.833 0.00039 *** x 1.2780 0.2984 4.284 0.00267 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.929 on 8 degrees of freedom Multiple R-squared: 0.6964, Adjusted R-squared: 0.6584 F-statistic: 18.35 on 1 and 8 DF, p-value: 0.002675 how can this be interpreted

Answer 26

F-statistic = 18.35, corresponding p-value = .002675. Since this p-value is less than .05, the model as a whole is statistically significant. Multiple R-squared = .6964. This tells us that 69.64% of the variation in the response variable, y, can be explained by the predictor variable, x. Coefficient estimate of x: 1.2780. This tells us that each additional one unit increase in x is associated with an average increase of 1.2780 in y. We can then use the coefficient estimates from the output to write the estimated regression equation: y = 11.1432 + 1.2780*(x)

Question 27

why use the plot() function to plot the diagnostic plots for the regression model

Answer 27

These plots allow us to analyze the residuals of the regression model to determine if the model is appropriate to use for the data.

Question 28

whats A multivariable linear regression

Answer 28

includes more than one independent variable to predict the dependent variable

Question 29

linear regression denotation

Answer 29

y = β0 + β1x where y is the outcome, x is our explanatory variable

Question 30

If x is not continuous, but a categorical variable with 3 levels e.g ‘A’, ‘B’ and ‘C’. Then the linear regression is denoted as

Answer 30

y = β0+β1xlevel1+β2xlevel2, where xlevel1 = 1 if x is equal to the first level and zero otherwise, xlevel2 = 1 if x is equal to the second level and zero otherwise. If x is equal to the third level then y = β0 only

Question 31

## ## Call: ## lm(formula = pain ~ drug, data = migraine) ## Residuals: ## Min 1Q Median 3Q Max ## -1.7778 -0.7778 0.1111 0.3333 2.2222 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 3.6667 0.3629 10.104 4.01e-10 *** ## drugB 2.1111 0.5132 4.114 0.000395 *** ## drugC 2.2222 0.5132 4.330 0.000228 *** ## --- ## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 ## ## Residual standard error: 1.089 on 24 degrees of freedom ## Multiple R-squared: 0.498, Adjusted R-squared: 0.4562 ## F-statistic: 11.91 on 2 and 24 DF, p-value: 0.0002559 Write the equation and interpret the model fitted above.

Answer 31

so the equation is y = all coefficients added together y = 3.6667 + 2.1111 (drugB) + 2.2222 (drugC) r2 is 0.498, so only 50% of variation explained

Question 32

generalised linear model (GLM),

Answer 32

allows the linear model to be related to the outcome via a link function and is much more flexible than a simple or multivariable linear model.

Question 33

logistic regression model

Answer 33

used for binary outcome

Question 34

## ## Call: ## glm(formula = chd ~ obesity, family = binomial(link = "logit"), ## data = CHD) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -1.3396 -0.9257 -0.8558 1.4021 1.7116 ## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -1.92831 0.61692 -3.126 0.00177 ** ## obesity 0.04942 0.02318 2.132 0.03302 * ## --- ## Signif. codes: 0 ***’0.001 ** 0.01 * ’0.05 ’.’ 0.1 ’ ’ 1 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 596.11 on 461 degrees of freedom ## Residual deviance: 591.53 on 460 degrees of freedom ## AIC: 595.53 ## ## Number of Fisher Scoring iterations: 4 interpret this r output?

Answer 34

obesity has a significant association due to * = 0.05

Question 35

## ## Call: ## glm(formula = chd ~ obesity + sbp, family = binomial(link = "logit"), ## data = CHD) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -1.5239 -0.9078 -0.7921 1.3084 1.7982 ## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -3.938978 0.839783 -4.690 2.73e-06 *** ## obesity 0.029783 0.024085 1.237 0.216257 ## sbp 0.018118 0.004972 3.644 0.000268 *** ## --- ## Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 ’.’ 0.1 ’ ’ 1 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 596.11 on 461 degrees of freedom ## Residual deviance: 577.80 on 459 degrees of freedom ## AIC: 583.8 ## ## Number of Fisher Scoring iterations: 4

Answer 35

when we include blood pressure as a predictor, obesity is no longer significant. ' ' = significant level of 1

Question 36

Poisson regression

Answer 36

is another type of GLM where the response variable has a Poisson distribution. In Poisson regression, the response variable Y is a count

Question 37

Spearman’s rank correlation rho data: A and B S = 2, p-value = 0.3333 alternative hypothesis: true rho is not equal to 0 sample estimates: rho 0.8 interpret this output

Answer 37

Spearman rank correlation is 0.8 and the corresponding p-value is 0.3333. This indicates that there is a positive correlation between the two vectors. However, since the p-value of the correlation is not less than 0.05, the correlation is not statistically significant.

Question 38

general equation for y and x relationship

Answer 38

y = a + bx b is the slope of the line a is the constant or intercept

Question 39

F-Test of overall significance in regression is a test of

Answer 39

whether or not your linear regression model provides a better fit to a dataset than a model with no predictor variables. The F-Test of overall significance has the following two hypotheses: Null hypothesis (H0) : The model with no predictor variables (also known as an intercept-only model) fits the data as well as your regression model. Alternative hypothesis (HA) : Your regression model fits the data better than the intercept-only model.