If you mix together an equal number of F+ and F− cells, how would you expect the proportions to change over time? In other words, do you expect an increase in the relative proportion of F+ or of F− cells? Explain your answer.
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If you mix together an equal number of F+ and F− cells, how would you expect the proportions to change over time? In other words, do you expect an increase in the relative proportion of F+ or of F− cells? Explain your answer.
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- The relationship between weight (in kilograms) and gender was estimated as below in a linear format. (Male was coded as 0 and Female was coded as 1.) Weight = -10.23 * Female + 66.73 (The p-value for the coefficient of Female is 0.03.) Base on the above equation, which of the following statements are true? (Select all that apply. There can be more than one answer.) Answer options: In the sample used to estimate the equation, the average weight of females is 66.23 kilograms In the sample used to estimate the equation, the average weight of females is 56.5 kilograms In the sample used to estimate the equation, the average weight of males is 66.73 kilograms Females are expected to weigh 10.23 kilograms less than malesWhat does P-value indicate? In statistical analysis the results for two sets of data are presented with P < 0.01 and P<0.005. What does it mean?With reference to this data what is the number of samples (n)?
- The mean weight of loaves of bread produced at the bakery where you work is supposed to be 1 pound. You are the supervisor of quality control at the bakery, and you are concerned that new employees are producing loaves that are too light. Suppose you weigh an SRS of bread loaves and find that the mean weight is 0.975 pound. What are the appropriate hypotheses for performing this significance test? Ho : x = 1; H. : x < 1 where i = the sample mean weight (in pounds) of bread loaves produced at the bakery. Ho : u = 1; H. : µ < 1 where u = the true mean weight (in pounds) of bread loaves produced at the bakery. Ho : µ < 1; H, : H = 1 where u = the true mean weight (in pounds) of bread loaves produced at the bakery. Ho : u = 1; H. : p = 0.975 where u= the true mean weight (in pounds) of bread loaves produced at the bakery. Ho : u = 1; H. : u # 1 where u= the true mean weight (in pounds) of bread loaves produced at the bakery.Please provide examples of the (Hardy-Weinberg Law P^2 + 2pq + q^2 = 1)Draw a graph of V0 vs. [S] (Michaelis-Menten plot) for these data. Put both sets of data on the one graph, use a different symbol of each plot and indicate which set of data is which. Make sure your graph has appropriate axis labels and units. You do not need to insert trend lines (as there is no rectangular hyperbola trendline in EXCEL).
- Salt Concentration (%) Transmittance (%) 0 77.23 3 79.23 6 80.39 9 82.71 12 85.66 15 87.55 What is the dependent variable and the independent variable? what patterns do you observe based on the information in the table?The electrophoresis gel shown below had a 4th lane (cut off and not shown) which contained 7 proteins of known molecular weight. A plot of these proteins was made with log MW on the y-axis and migration distance (cm) on the x-axis.The equation for the linear regression line is: Y = -0.1 X + 4.79If the distance between the well and the 3rd band of the Red sample is 4 cm, what is the approximate MW, expressed in daltons, of the protein at that location?What is the relationship of the two variables illustrated in the graph above?
- 1) You compare the height and weight of 5 adult women. You get the following results:height 60 67 62 70 66weight 119 143 131 155 136(a) Construct a scatter plot of height (x) vs. weight (y)(b) Calculate the following (show your work for SScp):SSx SSy SScp ̄x ̄y(c) Calculate the correlation coefficient (r)2) Use the information from problem 1. Perform a complete test of the hypothesis that the populationcorrelation coefficient (ρ) is 0. Show all steps (note - obviously this should be a one sided test! Make sure youknow why!).3) Now let's assume you wanted to predict weights from heights. In other words, now let's use the same datafrom problem (1) and do a regression instead.(a) Calculate b0 and b1.(b) Carefully draw your least square regression line on the plot you made in 1(a). (Don't just “sketch”,be a little careful).4) (a) Now do a significance test of H0: β1 = 0. Show all your calculations (including your residualcalculations). Again, note that this should be a one sided test…Why is there a blue scale?Using the rule of 1500, what is a more precise rate? (hint: divide 1500 by number of small boxes)