pls help

# CG Q0a # Read the data file bikeshare.csv into R and name the object bikes. ####### As usual, don't forget the strings = T argument.

# CG Q1 # Run str() on the bikes data frame to see that ###### there are quantitative variables and to confirm that the ###### strings = T argument reads in character data as a factor.

str(bikes)

# Question 2 # a simple logistic regression

# CG Q2a # Using glm(), build a logistic regression model that has ####### high count (greater than 7,000) as the response and ####### the temperature variable as predictor. ####### Name your fitted model logisticFit. ####### Hint: To model the binary response, use cnt>7000 as the response.

# CG Q2b # Use the summary() function on logisticFit ###### to access the results of the regression.

summary(logisticFit)

# CG Q2c # Print the coefficient for temperature. Use coef(logisticFit) ####### followed by the name of that coefficient in quotes ####### inside square brackets as you did in your linear regression HW.

coef(logisticFit)["temp"]

# CG Q2d # Now print the multiplicative effect of temperature ####### on the odds of ride count being higher than 7,000 by ####### wrapping the line of code from Q2c in the exp() function.

exp(coef(logisticFit)["temp"])

# CG Q2e # Using the result from Q2d, determine how the odds of the ####### ride count being higher than 7,000 change with a 1 degree ####### increase in temperature. ####### A: up 1 degree, B: up 13 degrees, C: up by 1%, D: up 13%, E: down 12% ####### Use paste("letter") to indicate your answer. For example, ####### if you think A is the correct answer, type paste("A").

# CG Q2f # Find the R-squared for the regression. ####### In your calculation, use logisticFit$deviance and ####### logisticFit$null.deviance and not the numbers printed ####### in the summary output for these.

# Question 3 # Predict probability of success

# CG Q3a # You will predict the probability of more than 7,000 rides ####### when it's 25 degrees celsius day. ####### Create a data frame defining this value and name it newdata.

# CG Q3b # Use the predict() function to predict the probability of ####### demand for more than 7,000 rides using newdata from Q3a.

predict(logisticFit,newdata)

# Question 4 # a logistic regression with multiple predictors

# CG Q4a # Build a logistic regression model that has high ride count ###### as the response modeled by the weather variables ###### weathersit, temp, hum, and windspeed. ###### Use an additive model (don't model interactions or anything fancy). ####### Name your fitted model logistic2.

logistic2 <- glm(weathersit,temp,hum,windspeed,data = bikes)

# CG Q4b # Use the summary() function on logistic2 ###### to access the results of the regression.

# CG Q4c # Print the coefficient for windspeed. Use coef(logistic2) ####### followed by the name of that coefficient in quotes ####### inside square brackets as you did in your linear regression HW.

# CG Q4d # Now print the multiplicative effect of temperature ####### on the odds of ride count being higher than 7,000 by ####### wrapping the line of code from Q2c in the exp() function.

# CG Q4e # Using the result from Q4d, determine how the odds of the ####### ride count being higher than 7,000 change with a 1 mph ####### increase in windspeed (holding other predictors constant). ####### A: down 0.1 mph, B: up 0.9 mph, C: up by 1%, D: up 90%, E: down 10% ####### Use paste("letter") to indicate your answer. For example, ####### if you think A is the correct answer, type paste("A").

# CG Q4f # Find the R-squared for the regression. ####### In your calculation, use logistic2$deviance and ####### logistic2$null.deviance and not the numbers printed ####### in the summary output for these.

# Question 5 # Predict probability of success

# CG Q5a # You will predict the probability of more than 7,000 rides ####### on a clear, 25 degrees celsius day (77 degrees farenheit) ####### with 50% humidity and windspeed 5. ####### Create a data frame defining these values and name it newdata2.

# CG Q5b # Use the predict() function to predict the probability of ####### demand for more than 7,000 rides using newdata2 from Q5a.