Self Design & Simulated Data Points
Self Design & Simulated Data Points
ORDER NOW FOR CUSTOMIZED, PLAGIARISM-FREE PAPERS
You are required to design a study, analyse data, generate graphical representation of the data, and prepare a multi-media laboratory report.
It is a 750 word count lab report, however it requires you to design a study for example :” Effect of Age and Depth of Processing on Recall ” or you can come up with your own psychological model if you are more comfortable with another topic.
After which, data points have to be simulated through RStudio Cloud or RStudio, the script to simulate data is attached below.
Once done, you just have to piece together everything into a lab report. The script simulating data & lab report guide are both attached.
It is not easy, please read through and understand what is required before bidding.
ScripttoSimulateData.docx
BSC302_TSA_ResearchReportGuide.pdf
#########################################
### Script for BSC302 Research Report ###
#########################################
# You will have to change 3 (three) values in this script. Only change the values where it indicates you should do so.
# Do not touch anything in this section!!!! You will see the “Do not touch!!!!” message a lot. Sorry for being repetitive, but this warning saves a lot of time.
# indicates comments; that is, it is not code
# Before Step 1, modify the 3 numbers in the code according to your measurements. See explanation of this in the video.
# Step 1: Copy and paste the whole code in RStudio Cloud or RStudio.
# Step 2: Run the code.
# Step 3: The code generates a cvs file, which is your dataset.
# Step 4: Save the file in your computer.
###########################################################
### Generate numbers from a standard normal distribution###
###########################################################
# Do not touch anything in this section!!!!
set.seed(343) # necessary step for the generation of random numbers
participants = c (1:100) # it generates a vector with numbers 1 to 100
gender = c(rep(“female”,25),rep(“male”,25), rep(“female”,25),rep(“male”,25)) # it generates a vector with 50 instances of female and 50 instances of male
age = round(rnorm(n=100,mean=35,sd=5)) # it generates a vector with 100 ages
set.seed(989) # necessary step for the generation of random numbers
iv1_norm = rnorm (n=100, mean=0, sd=1) # it generates a vector of 100 numbers coming from a normal distribution with mean=0 and sd=1
set.seed(454) # necessary step for the generation of random numbers
iv2_norm = rnorm (n=100, mean=0, sd=1) # it generates a vector of 100 numbers coming from a normal distribution with mean=0 and sd=1
set.seed(101) # necessary step for the generation of random numbers
error = rnorm (n =100, mean=0, sd=1) # it generates a vector of 100 numbers coming from a normal distribution with mean=0 and sd=1
dv_norm = iv1_norm * .35 + iv2_norm * .1 + error * .6 #it generates a vector of 100 numbers for the dependent variable based on a combination of the independent variables.
#########################################
##### transform normal to raw ##########
#########################################
# Only in this section you need to change values. Do not touch anything in the sections above.
# The values generated above come from a *standard* normal distribution; therefore, there are negative and positive numbers centred around zero.
# This section transforms those values into the scale of the measurements you chose for your study.
# Change this value!!! First value to change.
mean_iv1 = 30 # This is the mean of your independent variable 1. Change this value based on the appropriate value for the measurement you chose.
sd_iv1 = mean_iv1 * .1 # This generates the standard deviation of independent variable 1. Do not touch!!!!
# Change this value!!! Second value to change.
mean_iv2 = 50 # This is the mean of your independent variable 2. Change this value based on the appropriate value for the measurement you chose.
sd_iv2 = mean_iv2 * .1 # This generates the standard deviation of independent variable 2. Do not touch!!!!
# Change this value!!! Third value to change. This is the last value to change!!!
mean_dv = 120 # This is the mean of your dependent variable. Change this value based on the appropriate value for the measurement you chose.
sd_dv = mean_dv * .1 # This generates the standard deviation of independent dependent variable. Do not touch!!!!
iv1 = round(mean_iv1 + (iv1_norm * sd_iv1),0) # This creates raw values and rounds the values of your independent variable 1. Do not touch!!!!
iv2 = round(mean_iv2 + (iv2_norm * sd_iv2),0) # This creates raw values and rounds the values of your independent variable 2. Do not touch!!!!
dv = round(mean_dv + (dv_norm * sd_dv),0) # This creates raw values and round the values of your dependent variable. Do not touch!!!!
df = data.frame (participants,gender,age,iv1,iv2,dv) # This creates a data frame with all the vectors generated above. Do not touch!!!!
write.csv(df,”df.csv”, row.names = FALSE) # This generates a csv file called df, and it saves it. Do not touch!!!!
iv1 # this shows you the 100 values of independent variable 1. Do not touch!!!!
iv2 # this shows you the 100 values of independent variable 2. Do not touch!!!!
dv # this shows you the 100 values of the dependent variable. Do not touch!!!!