- the last one ‘design meets all the criteria...’
Answer:
By running multiple regression with dummy variables
Explanation:
A dummy variable is a variable that takes on the value 1 or 0. Dummy variables are also called binary
variables. Multiple regression expresses a dependent, or response, variable as a linear
function of two or more independent variables. The slope is the change in the response variable. Therefore, we have to run a multiple regression analysis when the variables are measured in the same measurement.The number of dummy variables you will need to capture a categorical variable
will be one less than the number of categories. When there is no obvious order to the categories or when there are three or more categories and differences between them are not all assumed to be equal, such variables need to be coded as dummy variables for inclusion into a regression model.
Answer:
The lowest point of the curve is at 239+42.5 ft where elevation is 124.16 ft.
Explanation:
Length of curve is given as
is given as
The K value is given from the table 3.3 for 55 mi/hr is 115. So the value of A is given as
A is given as
With initial grade, the elevation of PVC is
The station is given as
Low point is given as
The station of low point is given as
The elevation is given as
So the lowest point of the curve is at 239+42.5 ft where elevation is 124.16 ft.
Answer:
Answer for the question:
"Show that for a linearly separable dataset, the maximum likelihood solution for the logisitic regression model is obtained by finding a weight vector w whose decision boundary wx.
"
is explained in the attachment.
Explanation:
Answer:
41.5° C
Explanation:
Given data :
1025 steel
Temperature = 4°C
allowed joint space = 5.4 mm
length of rails = 11.9 m
<u>Determine the highest possible temperature </u>
coefficient of thermal expansion ( ∝ ) = 12.1 * 10^-6 /°C
Applying thermal strain ( Δl / l ) = ∝ * ΔT
( 5.4 * 10^-3 / 11.9 ) = 12.1 * 10^-6 * ( T2 - 4 )
∴ ( T2 - 4 ) = ( 5.4 * 10^-3 / 11.9 ) / 12.1 * 10^-6
hence : T2 = 41.5°C
