Give a brief analysis on the following Chi square for cross 1 (Wild type female × white eyes vestigial wings male) (1st image) and the chi square 2 (White eyes vestigial female × wild type Male)(2nd image).The chi-square value for cross1 is 7.259 and for cross 2 is 59.301. White eyes withnormal wingsRedeyeswithRedeyeswithWhite eyes withnormal wingsvestigial wingsvestigial wingsObserved frequency505310(0)Expected frequency(E)66.937522.312522.31257.4375Deviation (O-E)-16.9375-16.312530.68752.5625Thus, X will be(-16,9375)²(-16.3125)²+(30.6875)2(2.5625)-+66.937522.312522.31257.4375266.098286.87966.9375941.723+22.31256.566+7.437522.3125= 4. 286 + 11.926 + 42. 206 + 0. 88359. 301Hence, chi-square (X ) is 59.301. Red eyes withnormal wingsRed eyes withvestigial wingsWhite eyes withnormal wingsWhiteeyeswithvestigial wingsObserved frequency5813121(0)Expected frequency(E)47.2515.7515.755.25Deviation (O-E)10.75-2.75-3.75-4.25Thus, x?will be(10.75)-(-2.75)+(-3.75)+(-4.25)-+47.2515.7515.755.257.5625+14.0625+15.7518.06255.25115.562547.2515.75= 2.446 + 0. 48 + 0. 893 + 3.44= 7. 259Hence, chi-square (X)) is 7.259.

Given: Result of Chi-square test

Give a brief analysis on the following Chi square for cross 1 (Wild type female × white eyes vestigial wings male) (1st image) and the chi square 2 (White eyes vestigial female × wild type Male)(2nd image). The chi-square value for cross1 is 7.259 and for cross 2 is 59.301.

Give a brief analysis on the following Chi square for cross 1 (Wild type female × white eyes vestigial wings male) (1st image) and the chi square 2 (White eyes vestigial female × wild type Male)(2nd image). The chi-square value for cross1 is 7.259 and for cross 2 is 59.301.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

You collect GPA data from samples of students from 5 different colleges at the university. To analyze your data with a parametric test, you would use a
From a test cross you get 152 green, hairless; 23 green, hairy; 148 white, hairy, and 27 white, hairless. Calculate the chi squared to test the independent assortment hypothesis and determine if these genes assort independently.
Several states have argued that the 65-mph speed limit has no justification and have refused to enforce it. The federal Department of Transportation (DOT) believes that the 65-mph limit saves lives. To illustrate its contention, the department regressed the number of traffic fatalities (Y) last year in a state on the state’s population (X1), the number of days of snow cover (X2), and the average speed of all cars (X3). The results are shown below. From the results shown above, write the regression equation. What proportion of variation in the number of traffic fatalities can be explained by the regression model?
The highway department wants to estimate the proportion of vehicles on a Toll road at KM 25 between midnight and 5:00 A.M. The department head wanted to see how many 18-wheel tractor trailers passed along the road. These estimates will be used to determine road improvement and construction considerations and in road patrol planning. The department head asked researchers to calculate the estimated vehicles at different locations on the inter-island road over the past 7 days. Of the 3,481 vehicles counted, 927 were 18 wheelers. However, out of a total of 927, there were 74 who had accidents, so the sample was calculated based on the results of reducing the number of accidents. Determine the point estimate for the proportion of vehicles traveling on KM 25 during this time period which are 18 wheelers! (Assume the confidence level is 99%). What is the benefit of estimating the estimated value in this case (analyze the answers you have obtained)?
Select the description that most completely classifies the given variable.Number of wins by the NFL teams in the NFC Central divisionA) Quantitative, nominal B) Quantitative, interval C) Qualitative, interval D) Quantitative, ratio
A study was carried out to determine the influence of a trace elementfound in soil on the yield of potato plants grown in that soil, defined asthe weight of potatoes produced at the end of the season. A large fieldwas divided up into 14 smaller sections for this experiment. For eachsection, the experimenter recorded the amount of the trace elementfound (in milligrams per metre squared) and the corresponding weightof the potatoes produced (in kilograms). This information is presentedin the worksheet ‘Dataset5’ in the Excel document. Define X as thetrace element amount and Y as the yield.1. Draw a scatterplot of your data set.Page 52. Calculate the coefficients of a linear equation to predict the yieldY as a function of X.3. Calculate the correlation coefficient for the paired data values.4. Set up the framework for an appropriate statistical test to establish if there is a correlation between the amount of the trace element and the yield. Explain how having the scatterplot referredto…
Does the following data show inverse variations? \table[[x,y],[12,20],[3,5],[0.75,1.25]]
3) You measure the length of 5 radish seedlings at 7 days and 10 days and get the following results in mm (do NOT use R except for part (d)): seedling #: 1 2 3 4 S 5 days: 30 20 38 49 32 33.8 10.686 7 days: 35 27 46 58 34 40 12.145 difference: -5 -7 -8 -9 -2 -6.2 2.775 (a) Is there a difference in length? (Note: this should be one-sided. Why??) (b) Repeat (a), but now use a regular (unpaired) t-test. (c) What happened in (b)? (d) Verify the normal distribution assumption for the paired t-test you did in (a). (You may use R for this part).
Several states have argued that the 65-mph speed limit has no justification and have refused to enforce it. The federal Department of Transportation (DOT) believes that the 65-mph limit saves lives. To illustrate its contention, the department regressed the number of traffic fatalities (Y) last year in a state on the state’s population (X1), the number of days of snow cover (X2), and the average speed of all cars (X3). The results are shown below. Table 10: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .821 a .780 .613 3.258 a. Predictors: (Constant), population, days of snow, average speed Table 11: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.4 2.957 .473 .584 Population .00029 .00003 .0034 9.667 .000 Days of Snow 2.4 .62 .759 3.871 .000 Average Speed 10.3 1.1…
7.8km 16km SA=
The question is in the image
5