Determine if the following prompt describes an experiment or observational study. Waymo monitors passenger satisfaction in its autonomous vehicles over a one-year period in order to examine whether passenger ratings are correlated with the length of the ride.

A group of 80 homeowners wants to investigate whether the type of paint and room lighting affect the perceived brightness of a room. Half of the participants were put in a room with matte finish paint, and the other half were put in a room with gloss finish paint. Within each paint finish group, 10 participants evaluated the room under LED lighting, 10 under incandescent lighting, 10 under fluorescent lighting, and 10 under natural daylight. The design of this experiment is a 2×4 factorial design with Paint Finish having two levels and Lighting Type having four levels. At the end of the evaluation, participants measured and reported the perceived brightness of the room in lumens. The data is shown below. LED Incandescent Fluorescent Natural Matte finish 270 180 330 105 Matte finish 235 355 260 140 Matte finish 40 320 85 175 Matte finish 115 285 155 10 Matte finish 80 320 120 245 Matte finish 10 20 15 140 Matte finish 10 320 50 10 Matte finish 80 145 120 210 Matte finish 120 75 155 210 Matte finish 290 285 330 220 Gloss finish 140 250 185 220 Gloss finish 180 460 220 360 Gloss finish 190 250 220 220 Gloss finish 285 355 325 95 Gloss finish 75 250 115 255 Gloss finish 60 250 90 325 Gloss finish 60 425 100 395 Gloss finish 350 425 395 360 Gloss finish 355 145 395 430 Gloss finish 140 215 185 360 Conduct an ANOVA. Use a level of significance (alpha) of 0.05. What is the F-statistic for the Paint Finish main effect? Round to one decimal place [a] What is the F-statistic for the Lighting Type effect? Round to one decimal place [b] What is the F-statistic for the interaction between Paint Finish and Lighting Type? Round to one decimal place [c]

Spiritual Airlines is operating a 200-seat flight from Phoenix to Boston during the peak holiday season, where demand far exceeds the number of available seats. Each plane ticket is priced at $350. Due to the high demand, the airline will be able sell all 200 tickets. Historically, however, some passengers fail to show up for their flights. The airline estimates that the number of no-show passengers follows a uniform distribution between 5 and 20. Assume there are no stand-by passengers, and the airline does not refund no-show tickets. For instance, if 10 passengers do not show up, the airline will fly with 190 passengers but still receive revenue for all 200 tickets sold. What is the airline’s revenue for this flight? Hint: Do not overthink this!

The shadow price of a constraint represents the change in the optimal objective value if the right-hand-side of the constraint increased by ________________.

In a randomized design study involving one factor, the F-statistic in ANOVA is calculated by dividing the __________ variance by the within-group variance.

Below are the results from a 3×5 factorial experiment with 150 observations in total. Please complete the table. Report df as an integer and all other values to one decimal place. df SS MS F Factor A 2 965.6 482.8 99.1 Factor B [a] [b] [c] [d] A*B 8 12.9 1.6 0.3 Error 135 657.6 4.9   Total [e] 2266.7    

Save your Excel file as “FirstInitialLastName – Midterm Exam.xlsx” (e.g., “TLekwijit – Midterm Exam.xlsx”) Submit Your Excel File. Failure to submit your Excel file for the Midterm Exam will result in an automatic 50% penalty on your final score.

You have a file students.txt with the following content: ID: 1234[Math:85, Science:90, English:88]ID: 5678[Math:78, Science:82, English:80]ID: 9101[Math:92, Science:88, English:91]Write a command line that would output: ID: 1234 [Math:85, Science:90, English:88]ID: 5678 [Math:78, Science:82, English:80]ID: 9101 [Math:92, Science:88, English:91]  

Write a function called get_sentence_one_hot that, given a sentence (represented as a list of word tokens) and a vocabulary (a unique list of tokens/words), returns a list of one-hot encoded vectors, where each word in the sentence is represented by a one-hot encoding based on the vocabulary. In this representation: Each word in the sentence is encoded as a one-hot vector, where the index corresponding to that word in the vocabulary is set to 1, and all other indices are set to 0.  If a word from the sentence does not appear in the vocabulary, it should be encoded as a vector of all zeros. Example: words = [“cat”, “dog”, “cat”, “zebra”]vocabulary = [“cat”, “dog”, “parrot”]get_sentence_one_hot(words, vocabulary) = [    np.array([1, 0, 0]),  # “cat” is the first word in the vocabulary    np.array([0, 1, 0]),  # “dog” is the second word in the vocabulary    np.array([1, 0, 0]),  # “cat” again    np.array([0, 0, 0])   # “zebra” is not in the vocabulary]  

A 60 yo diabetic client is being seen in the eye clinic and has an intraocular pressure (IOP) of 28 mm Hg. What action would the nurse anticipate?