Biology181 i need complete asap | Biology homework help
- Below are four pairs of plots, each depicting two linear models. In each pair of plots, the linear
model on the left shows the linear relationship between the categorical independent variable (absence or presence of GF-L) and the average activity (%) of Receptor 1 (R1); the linear model on the right shows the linear relationship between the categorical independent variable (absence or presence of GF-L) and the average activity (%) of Receptor 2 (R2). Match each pair of figures and their corresponding linear models with the claim they support. Claims
- Claim 1: GF-L binds only to R1 in the cell signaling pathway.
- Claim 2: GF-L binds only to R2 in the cell signaling pathway.
- Claim 3: GF-L binds to R1 and R2 in the cell signaling pathway.
- Claim 4: GF-L does not bind to R1 or R2 in the cell signaling pathway.
Figure Pairs A-D, Long description Figure Pairs A-D. The relative activity of Left: Receptor 1 (R1) and Right: Receptor 2 (R2) in the absence or presence of Growth Factor L (GF-L). Filled circles (R1) and diamonds (R2) represent the mean relative activity of each receptor. Step 2: Model the effects of GF-L on the activity of R1 and the activity of R2.
Excel tutorials:
● #9 Saving/Uploading Excel Files - Mac or #9 Saving/Uploading Excel files - Windows ● #15 Modeling a Linear Relationship with a Categorical Independent Variable; ● #16 Plotting Linear Model of Categorical Variable; To learn how the presence of GF-L relates to the cancer of spotted gliders, we must discover how GF-L affects the activities of receptors R1 and R2. We performed an experiment in which GF-L was added to a tumor cell. From that experiment, I estimated the activity of R1 and R2 in the presence or absence of GF-L. You must use these data to determine how GF-L affects the activities of each type of receptor. The two conditions in this experiment—presence or absence of GF-L—represent categories rather than continuous values. Therefore, you will need to use a linear model with a categorical independent variable to predict the mean activity of a receptor in the presence or absence of GF-L. A linear model with a categorical independent variable can be described as follows: μ = aX + b μ is the expected activity of a protein (%) for a given category; a is the slope of the linear relationship between the category and the mean activity; X is the categorical independent variable representing the presence or absence of GF-L; b is the intercept of the linear relationship between the category and the mean activity. This linear model appears identical to that used for a continuous independent variable. However, the value of X for a categorical variable can be only 0 or 1, where 0 arbitrarily represents one category and 1 arbitrarily represents the other category. Scientists routinely code categories alphabetically; for example, because "Absence of GF-L" comes alphabetically before "Presence of GF-L", we should code observations in the former group as 0 and observations in the latter group as 1. From here on out, we will code categories alphabetically, so observations in the "Absence of GF-L" will be coded as 0 and observations in the "Presence of GF-L" will be coded as 1. In science, the recoding of a categorical variable as 0 or 1 is referred to as dummy coding.
The Dummy Code may look like this:
=IF(logical_test, [value_if_true], [value_if_false]) Or =IF(categorical variable data column=“Absent”,0,1)
Example:
Imagine that someone observed the body mass of 5 mice from each of two populations, a northern population and a southern population. How would we dummy code a variable to represent these two categories (northern vs. southern)? The example Excel Spreadsheet below illustrates the standard method of dummy coding a categorical variable in Excel. The two categories of mice, northern and southern, have been coded as 0 and 1, respectively. For these data, you would calculate the slope, intercept, and standard deviation of the linear relationship
by entering the following functions in Excel:
Dummy Code:
=IF(A2:A11=”northern”,0,1)
Slope:
=SLOPE(B2:B11, C2:C11)
= -2.0 g
Intercept:
=INTERCEPT(B2:B11, C2:C11)
= 25.3 g
Standard deviation:
=STEYX(B2:B11, C2:C11)
= 2.2 g
A B C
1 Population Body mass (g) Dummy Code 2 northern 25.9 0 3 northern 26.1 0 4 northern 23.8 0 5 northern 24.4 0 6 northern 26.1 0 7 southern 25.3 1 8 southern 26.9 1 9 southern 20.1 1 10 southern 21.1 1 11 southern 22.7 1 Therefore, the linear model is μ = -2.0X + 25.3 where X equals 0 for northern mice and 1 for southern mice. With this model, we can calculate the mean body mass of mice in each category. In each case, we enter the value of X and solve for the mean of absent or present (μ).
To compute the mean body mass of a northern mouse, we enter a value of 0 for X:
μ = -2.0(0) + 25.3 = 25.3 g Additionally, we can compute the mean body mass of a southern mouse by entering a value of 1 for X: μ = -2.0(1) + 25.3 = 23.3 g Both means have a standard deviation (σ) of 2.2 g. Part 1 - Effect of GF-L on the Activity of Receptor 1 (R1) Directions: For questions 2-8, download the “CB Act 2 Workbook” from your Canvas and refer to the sheets titled “Q2-8 GF-L and R1.” This sheet contain the activity (%) of Receptor 1 (R1) in the absence or presence of GF-L (sample size = 10 measurements per category, absence or presence of GF-L). Create a column of dummy codes to represent the two conditions, with 0 representing the absence of GF-L and 1 representing the presence of GF-L. Use Excel for calculations, modeling, and graphing.