Solve the following questions correctly.,,,

Question 1

Question 1

Which of the following is NOT TRUE about Multiple Regression

a. Assumes a linear relation with each variable.

b. Can only be used with numeric variables.

c. Assumes that the terms in the model are additive.

d. Is a popular method for Value Estimation.

Question 2

Multi-collinearity

a. Is a serious problem for Causal Analysis.

b. Means that you have many variables.

c. Means that the model can only have linear functions.

d. Is a popular method for Value Estimation.

Change your responses to resubmit

Question 3

In building regression models, nominal variables, such as Gender or Ethnicity,

a. Can be added to the model by assigning numbers to each label.

b. Cannot be included in regression models because they are not numeric.

c. Are easiest to include for binary variables where Yes = 1 and No = 0.

d. None of the above is true.

Question 4

Residuals Plots are useful to explore

a. patterns between our prediction errors and the variables that were not used in the model.

b. Possible outliers.

c. Whether there is a non-linear (curved) relation with a variable in the model.

d. All of the above.

Question 5

You have fitted a multiple regression model to predict house prices and obtained an R-square of 0.63.

a. The model explains 63% of the variation in house prices.

b. The model gives accurate predictions of house prices 63% of the time.

c. The model should give accurate forecasts because the R-square exceeds 50%

d. None of the above is correct.

Question 6

If the scatter chart appears curved,

a. Then we cannot use multiple regression to fit a model.

b. We can fix this by including a binary variable.

c. We can approximate the curve by adding a polynomial with variables such as x2 (x-squared) or x3 (x-cubed).

d. None of the above is true.

Question 7

If a variable has a negative coefficient

a. Then the R-square will be lower than if this variable was not included.

b. Then the predicted value will decrease when this variable increases.

c. Something is wrong when a coefficient is negative.

d. None of the above.

Question 8

A model to estimate House Price based upon the size of the house (Space) and the number of bedrooms (Beds), resulted in the following output. A 1,000 square foot house with 3 bedrooms

a. Is likely to sell for exactly $420,620.

b. is likely to sell for 62.3% of the estimated Price.

c. is likely to sell for at least $420.620.

d. is unlikely to sell for more than $163,365 above the estimated Price.

Question 9

Classification models are

a. An example of Supervised Learning.

b. The same as Clustering models.

c. Best solved using Multiple Regression.

d. None of the above.

Question 10

Applying multiple regression to classification presents challenges because

a. The outcome is binary.

b. Only gives estimates of the probability of being in a group (class).

c. Can give probability outcomes that are not between 0 and 1.

d. All of the above.

Question 11

Logistic regression is

a. Used to estimate the probability that a case is in a particular group.

b. Requires a decision rule to translate a probability into a classification.

c. Uses an S shaped curve to relate each variable to the binary outcome.

d, All of the above.

Question 12

A discriminant function

a. Is used to estimate the probability that a case is in a particular group.

b. Separates cases into many groups or clusters.

c. Is a function that separates the majority of values into two groups.

d. Is the same as cluster analysis.

Question 13

Errors in classification models

a.Are incorrect classifications.

b.May be measured in terms of various types of error rates.

c. Are not measured in terms of the size of the error.

d. All of the above.

Question 14

KNN is based upon

a. Finding K previous cases that are the most similar to the new case and using these cases to do the classification.

b. Finding K variables that are in common and using them in a logistic regression.

c. Finding K clusters of cases.

d. None of the above

Question 15

Methods that estimate the probability or likelihood that a case belongs to a group are incomplete without

a. A causal analysis that explains the contribution of each variable.

b. A decision rule to how to assign cases to groups based upon the probability.

c. A measure of error of the probability.

d. Nothing else is needed since you assign the case to the group if the probability exceeds 50%.

Question 16

Among methods for binary classification

a.Logistic regression is the most accurate.

b.Discriminant functions work best.

c.KNN is the method of last resort.

d.They all work reasonably well.

Question2

In this unit, we learned about the periodic table, elements, structure of the atom and how to predict properties of atoms based on their location in the periodic table. Answer the questions below.

Note: each question may have more than one part, so be sure thoroughly answer each part.

You must provide a logical explanation with sufficient detail to demonstrate mastery of the chemistry concept related to the question in order to pass. Although there is not a length requirement, answers less than 250 words will likely not offer sufficient detail to earn a passing grade.

I will need this completed within the next couple of days. Please no plagiarism and anyone willing to help will receive a very good review!

Using the periodic table provided in class, you can describe several properties about the atoms that make up a particular type of element. Take a look at magnesium, for example.Explain what the periodic table tells about magnesium, particularly in regards to 1. atomic symbol, 2. atomic number, 3. number of protons, 4. number of electrons, 5. electron configuration and 6. reactivity.

Define the term isotope. Using external resources, list the three most abundant isotopes for the element magnesium. Cite your source.Describe the similarities and differences between the isotopes you listed for magnesium. Similarities and differences should at minimum include 1. number of protons, 2. number of neutrons and 3. number of electrons.

Using sentences, describe the structure of a phosphorus-33 atom. Include in your description, information about the subatomic particles, including 1. names, 2. number, 3. charge and 4. location. Draw a model of this atom with the labeled parts: protons, neutrons, electrons, nucleus, 1stenergy level, 2ndenergy level and 3rd energy level.