# Answer true or false to each of the following statements and explain your answers. Polynomial regression equations are useful for modeling more complex curvature in regression equations than can be handled by using the method of transformations.

Question
Modeling
Answer true or false to each of the following statements and explain your answers. Polynomial regression equations are useful for modeling more complex curvature in regression equations than can be handled by using the method of transformations.

2020-11-08
The polynomial regression equation is useful when a linear regression equation fails to model the curvature between the response variable and the predictor variables. In order to make the polynomial regression equation easier to handle, a suitable method of transformations can be used on the variables. Also, when there are more complex curvature between the response and the predictor variables then the polynomial regression equations can be used than method of transformations. Thus, the statement “Polynomial regression equations are useful for modeling more complex curvature in regression equations that can be handled by using the method of transformations.” is ul(True).

### Relevant Questions

Answer true or false to each of the following statements and explain your answers.
a. Polynomial regression equations are useful for modeling more complex curvature in regression equations than can be handled by using the method of transformations.
b. A polynomial regression equation can be estimated using the method of least squares, the same method used in multiple linear regression.
c. The term “linear” in “multiple linear regression” refers to using only first-degree terms in the predictor variables.
Answer true or false to each of the following statements and explain your answers. A polynomial regression equation can be estimated using the method of least squares, the same method used in multiple linear regression.
Answer true or false to each of the following statements and explain your answers. A polynomial regression equation can be estimated using the method of least squares, the same method used in multiple linear regression.
We will now add support for register-memory ALU operations to the classic five-stage RISC pipeline. To offset this increase in complexity, all memory addressing will be restricted to register indirect (i.e., all addresses are simply a value held in a register; no offset or displacement may be added to the register value). For example, the register-memory instruction add x4, x5, (x1) means add the contents of register x5 to the contents of the memory location with address equal to the value in register x1 and put the sum in register x4. Register-register ALU operations are unchanged. The following items apply to the integer RISC pipeline:
a. List a rearranged order of the five traditional stages of the RISC pipeline that will support register-memory operations implemented exclusively by register indirect addressing.
b. Describe what new forwarding paths are needed for the rearranged pipeline by stating the source, destination, and information transferred on each needed new path.
c. For the reordered stages of the RISC pipeline, what new data hazards are created by this addressing mode? Give an instruction sequence illustrating each new hazard.
d. List all of the ways that the RISC pipeline with register-memory ALU operations can have a different instruction count for a given program than the original RISC pipeline. Give a pair of specific instruction sequences, one for the original pipeline and one for the rearranged pipeline, to illustrate each way.
Hint for (d): Give a pair of instruction sequences where the RISC pipeline has “more” instructions than the reg-mem architecture. Also give a pair of instruction sequences where the RISC pipeline has “fewer” instructions than the reg-mem architecture.
Answer true or false to each of the following statements and explain your answers. The term “linear” in “multiple linear regression” refers to using only first-degree terms in the predictor variables.
Which of the following statements is/are correct about logistic regression? (There may be more than one correct answer) Logistic regression can be used for modeling the continuous response variable with dichotomous explanatory variable. Logistic regression can be used for modeling the dichotomous response variable with dichotomous explanatory variable Logistic regression can be used for modeling the continuous response variable with dichotomous or other type of categorical explanatory variables. Logistic regression can be used for modeling the dichotomous response variable with dichotomous and not for continuous explanatory variables. Logistic regression can be used for modeling the dichotomous response variable with categorical explanatory variables and/or continuous explanatory variables.
Answer true or false to the following statements and explain your answers.
a. In multiple linear regression, we can determine whether we are extrapolating in predicting the value of the response variable for a given set of predictor variable values by determining whether each predictor variable value falls in the range of observed values of that predictor.
b. Irregularly shaped regions of the values of predictor variables are easy to detect with two-dimensional scatterplots of pairs of predictor variables, and thus it is easy to determine whether we are extrapolating when predicting the response variable.
The unstable nucleus uranium-236 can be regarded as auniformly charged sphere of charge Q=+92e and radius $$\displaystyle{R}={7.4}\times{10}^{{-{15}}}$$ m. In nuclear fission, this can divide into twosmaller nuclei, each of 1/2 the charge and 1/2 the voume of theoriginal uranium-236 nucleus. This is one of the reactionsthat occurred n the nuclear weapon that exploded over Hiroshima, Japan in August 1945.
C. In this model the sum of the kinetic energies of the two"daughter" nuclei is the energy released by the fission of oneuranium-236 nucleus. Calculate the energy released by thefission of 10.0 kg of uranium-236. The atomic mass ofuranium-236 is 236 u, where 1 u = 1 atomic mass unit $$\displaystyle={1.66}\times{10}^{{-{27}}}$$ kg. Express your answer both in joules and in kilotonsof TNT (1 kiloton of TNT releases 4.18 x 10^12 J when itexplodes).