# Login and Complete Homework 1 Quiz for Math 125 Course

Login and Complete Homework 1 Short Quiz for Math 125 Course….NEED AN A+ ON THIS QUIZ….

ONLY BID IF YOU ARE FAMILIAR WITH THE CONTENT OF THIS COURSE:

11.

EXPECTED STUDENT LEARNING OUTCOMES

Upon satisfactory completion of the course, students will be able to:

1.

Identify and interpret data/variable types, study designs, assumptions, and biases using appropriate

statistical terminology from written problem/literature description. (1.1-1.6)

2.

Organize, summarize, and interpret data using exploratory data analysis methodologies in tabular

and graphical form (e.g., frequency distribution table, dot plot, histogram, stem-and-leaf plot, box-

and-whisker plot, scatterplot, etc.). (2.1-2.3)

3.

Calculate and interpret probabilities by applying probability terminology, current probability

laws/theory (e.g., law of large numbers, Bayes theory) and counting techniques (permutation and

combination). (5.1-5.6,6.1-6.2)

4.

Identify, compute, and interpret measures for central tendency, dispersion, and probabilities for

discrete (e.g., geometric, binomial, Poisson) real world data sets. (3.1-3.5)

5.

Compute and interpret measures of and evaluate the assumptions for the central tendency,

dispersion, position, and probabilities for relevant continuous real-world data sets by applying

non-normal, normal and standard normal sampling distribution theory. (7.1-7.4)

6.

Use estimation theory to interpret and apply confidence intervals and compute sample sizes to

achieve desired confidence levels for real-world applications. (8.1-8.2,9.1-9.3)

7.

Perform bivariate analysis (correlation/regression) and interpret supporting graphics (scatterplot),

correlation coefficient, coefficient of determination, and linear regression model (best fit line

equation) for linear relationship, direction, strength, statistical significance, and applicability for

predictive modeling. (4.1-4.4, 12.3)

8.

Perform and effectively communicate univariate inference results for one or more groups

following formal accepted statistical practices (e.g., critical value, p-value, interval estimate, or

randomized simulation). (10.1-10.4, 11.1-11.4)

9.

Perform and interpret inference on categorical data using contingency tables and the chi-square

distribution (e.g., goodness of fit, independence, and homogeneous proportions).) (12.1 – 12.2)

10.

Demonstrate a working knowledge for using computational software to organize data, generate

statistical graphics, and perform statistical calculations. (Projec