Concept explainers
Refer to Data Set VALID.DAT (at www.cengagebrain.com) described in Table 2.16 (p. 38).
Answer Problem 5.59 using the ln(nutrient) transformation for each nutrient value. Is the normality assumption more appropriate for log-transformed or untransformed values, or neither?
Consider the nutrients saturated fat, total fat, and total calories. Plot the distribution of each nutrient for both the diet record and the food-frequency questionnaire. Do you think a
(Hint: Compute the observed proportion of women who fall within 1.0, 1.5, 2.0, and 2.5 standard deviations of the mean. Compare the observed proportions with the expected proportions based on the assumption of normality.)
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Fundamentals of Biostatistics
- Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?arrow_forwardWhat might a scatterplot of data points look like if it were best described by a logarithmic model?arrow_forwardGraph the following logistic function, first discussed in Example 5. Use window settings of 0,20 for x and 0,1,500,000 for y. Pt=1,200,0001+1199e-0.4tarrow_forward
- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardQUESTION 3 Consider the basic food items consumed by a rural household: Food item Consumption Price (N$) June 2021 July 2021 June 2021 July 2021 Bread(loaves) 25 26 10.30 10.70 Cooking oil(litres) 5 6 45.00 59.40 Rice(kg) 10 8 34.50 36.20 Meat(kg) 15 16 88.90 92.20 Required to: a)Which food item has had the lowest relative quantity of consumption increase between the months of June and July? b) Which food item has had the lowest relative price increase between the months Juneand July 2021? c) Calculate and interpret a Paasche quantity index for July 2021.arrow_forwardi have the following regression equation Log(total deaths per million) = -3.882 + 0.934Log(total cases per million) – 0.033(gdp per capita) + 0.014(aged 70 or older) - 0.050(hospital beds per thousand) + 0.011(human development index) How would I interpret the effect of aged 70 years or older, which is the percent of the population aged 70 or olderarrow_forward
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