Aerodynamics relies on both experimental and theoretical approaches to understand and predict the behavior of airflow around objects. Experimental methods involve testing models in wind tunnels or using computer simulations, while theoretical methods use mathematical models to analyze and predict aerodynamic phenomena.

Research and development are essential to advance the field of aerodynamics. New technologies, materials, and computational tools enable researchers to explore new frontiers and solve complex aerodynamic problems.

Aerodynamics plays a critical role in sports performance, particularly in sports that involve high speeds and aerodynamic drag, such as cycling, skiing, and swimming. By optimizing the aerodynamic shape and surface features of equipment and clothing, athletes can reduce drag and improve their performance.

Regulatory and policy issues also impact aerodynamics, particularly in industries such as aviation, automotive, and wind energy. Regulations and policies may limit the use of certain materials, shapes, or technologies that affect aerodynamics, and may also set standards for safety and efficiency.

Aerodynamics is a crucial discipline in aerospace engineering, where it impacts the design and performance of aircraft, missiles, and spacecraft. The shape and configuration of these vehicles must be optimized to achieve efficient flight, stability, and maneuverability, while minimizing drag and weight.

The geometry and speed of an object significantly influence its aerodynamic behavior. The shape and surface features of the object, as well as its velocity and orientation, determine the patterns of air pressure and shear that generate lift, drag, and other forces. Aerodynamicists use these principles to design efficient, stable, and safe vehicles and structures

Analyze and write about the results of a visual and an auditory lexical decision task. Analyze a large data set that includes students’ responses for both the visual and auditory modality. Lab is a write-up of these findings.

1. One large data file that includes all experimental trial responses for both the Visual and Auditory modality tasks. Note that the sample size for the visual experiment is 16 and for the auditory experiment is 15.

Presentedz with visual words and pseudo-words in one experiment and auditory words and pseudo-words in a second experiment. Respond whether you saw/heard a real word of English or not. In the visual task, remained fixated on a cross (i.e., ‘+’) presented in the centre of the screen, while the stimulus was visually presented either to the left or right of the fixation point. In the auditory experiment, you heard words and pseudo-words presented either to your left or right ear.

  Words Pseudo-words Number 60 60 Pronounceable N/A Yes Hemi-field/Ear Presentation 30 left/30 right 30 left/30 right String Length

Range: 5-7

Mean: 6.01 (SD = 0.71)

Range: 5-7

Mean: 5.85 (SD = 0.78)

Phoneme Length

Range: 5-7

Mean: 5.45 (SD = 0.59)

Range: 3-6

Mean: 4.58 (SD = 0.87)

Number of Morphemes 1 N/A

In each experiment, there were 60 words and 60 pseudo-words. It was the same 120 items presented in each experiment. Only their modality of presentation changed. Thirty words and 30 pseudo-words were presented to the left (side of the screen in the visual experiment, ear in the auditory experiment), while the other 30 words and other 30 pseudo-words are presented to the right. Side of presentation was balanced across participants using two lists. Each list contained the same set of words and pseudo-words; only presentation side was counter-balanced across lists.

Independent samples t-tests revealed no difference in orthographic length between the words and pseudo-words (t(117) = -1.59, p = 0.11); there is, however, a reliable phoneme length difference between the words and pseudo-words (t(104.28) = -6.37, p < 0.001). We also recorded whether the participant was a native speaker of English or not and whether they are right- or left-hand dominant. Feel free to use these factors in your analysis.

1. Given what you know about the organization of the visual and auditory pathways and lateralization of language processing, state, in very clear terms, what your predictions are. Provide specific predictions for your observation (dependent) variable(s), and select and name your predictor (independent) variable(s). 
2.  
3. Provide a detailed list of the processing steps in R that is performed to arrive at your results. For example, do you want to include incorrect trials trials in your analysis of both dependent variables? Do not provide the R code, but instead write out these steps in plain English.  
4. Provide a series of plots that provide an illustration of your results. You should minimally include a plot of the accuracy and reaction time results and this should be done for each modality. You may also included histograms of your reaction time distributions, but this is not necessary. Be sure your axes are appropriately labeled. 
5. Provide a table that presents your numerical findings in a clear manner. Remember to include both the sample-level averages and standard deviations per condition. 
6. Discuss your results in light of the predictions in Q1 and your findings in Q3 and Q4. Make specific reference to your predictions and the extent to which they are borne out. 
7. Get creative: Incorporate one other factor (e.g., native language, handedness). State your predictions and provide a breakdown of the data in terms of these factors. 
8. Design your own research question: Consider how you might use the same experimental design to understand another linguistic phenomenon and population (e.g. stop VOT, vowel length, lexical tone, Mandarin speakers, French speakers etc.). State your research question, and provide and motivate specific predictions for your dependent variable(s) as a function of your predictor variable(s). Note: Ideally, the new research question will still involve language lateralization. Consult the Gazzaniga readings online for ideas based on the particular specializations of each hemisphere. 

1 Introduction 

Your goal is to formulate a set of psycholinguistic hypotheses that interest you and use previously collected data to investigate these hypotheses. Your hypothesis should be plausible-ish, but feel free to be creative in what you investigate. Any statistical methods are ok to use in your analysis. It’s not required that you have statistically significant results, but it is a requirement to do the analyses properly. 

GIVE R CODES TO RUN

What I expect: To be written up in the form of a lab report that discusses your findings. 

• It should include an introduction, hypotheses, methods (what data was used, where it came from, what statistical tests were chosen and why, etc), results, discussion (what do the results mean scientifically), and a conclusion. 
• The number of tests to use, doing at least two analyses (complete with checks for assumptions). 
• You should make sure to use tables and plots when appropriate. And test assumptions! 
• Remember to motivate your choices and adequately illustrate your findings using plots and graphs. 

2 Working with the English Lexicon Project 

The English Lexicon Project (ELP) is a corpus of English lexical items with behavioral data for said items. That is, it contains accuracy and reaction times in lexical decision experiments and naming latencies in picture naming experiments. In addition to these latencies, you are also provided with the standard deviations for each word for each experiment-type, as well as the number of observations for each item. Basically, they’ve already run the experiments for you, so you just need to analyze them. 

The data frame for the ELP is massive. It has 12704 observations and 24 variables. But, it’s unlikely that you’ll be using that much data. Instead, the observations and variables you use will be determined by your experimental hypothesis and the particular analysis you select. 

Reading in a .csv file is slightly different than a .txt file, since it’s separated by commas and not tabs. You can use the following (remember to change file.choose() if you are on a system that uses a different command to bring up the user interface): 

data <- read.csv(file.choose(), header=T) 

Type head(data) to ensure you correctly loaded the file. And save your work often! 

2.1 Explanation of the columns in the ELP dataset 

There are a lot of columns in the ELP dataset. Here is a brief description of each column, and full descriptions can be found in the information on the ELP website. 

2.1.1 Independent variables 

Column Explanation 

Word The specific lexical item 

Length Number of orthographic characters (letters) composing the word Freq_Hal Word frequency 

Ortho_N Number of orthographic neighbours (e.g., ace = ate, act, ale, are, …) Phono_N Number of phonological neighbours (e.g., wings = wins, rings, …) Phono_N_H Number of phonological neighbours including homophones Freq_N Average frequency of an orthographic neighbourhood of a particular word 

Freq_N_P Average frequency of the phonological neighbourhood of a particular word 

BG_Sum Sum of the bigram count for a particular word 

BG_Mean Average bigram count for a particular word 

BG_Freq_By_Pos Sum of the bigram count (by position) for a particular word NPhon Number of phonemes in the standard pronunciation for a particular word2 

NSyll Number of syllables in the standard pronunciation for a particular word 

MorphSp Morphological (orthographic) composition of a particular word. See for a key to the symbols. 

MorphPr Morphological (phonetic) composition of a particular word. See for a key to the symbols. 

NMorph Number of morphemes 

2.1.2 Dependent variables 

Lexical decision task 

Column Explanation 

I_Mean_RT Mean reaction time for a particular word (in ms) across all participants 

I_SD Standard deviation of mean reaction time for a particular word (in ms) across all participants 

Obs Number of observations comprising reaction time data for a particular word 

I_Mean_Accuracy Average accuracy for a particular word across all participants 

Naming task 

Column Explanation 

I_NMG_Mean_RT Mean naming latency for a particular word (in ms) across all participants 

I_NMG_SD Standard deviation of mean naming latency for a particular word (in ms) across all participants 

I_NMG_Obs Number of observations comprising naming latency data for a particular word 

I_NMG_Mean_Accuracy Average accuracy for a particular word across all participants 

4 Hints 

You will probably want to do some serious subsetting of the data. Isolate just the rows and columns you want. Decide if you want to bin your lexical variables, e.g., create nominal categories such as high vs low frequency words, or use continuous measures, e.g., correlate your dependent variable against your independent variables. In the LADEC data, there are also rows with missing data (what R calls NA). Since many statistical tests prefer to have groups of equal sizes, you might need to remove rows with missing data. Do this using the na.omit() function, but only do this after you have selected which rows you’ll be working with. For instance, if the data was stored in d, and you only wanted columns 1, 2, 3, 40, 41, and 42, you can do 

d2 <- d[,c(1,2,3,40,41,42)] 

d2 <- na.omit(d2) 

to save just those columns to d2, and then strip out the rows with NA values. One way to get started is to pick a column from a dataset and think about whether there are groups you could subset out, based on data from other columns. Or, you can take that column and think about how it might correlate with another column in the dataset. These are only suggestions, though. There’s a lot of data here, and lots of things one could look at. You don’t need statistically significant results to write the paper, so you can be creative in what you test. 

Feel free to search the primary literature for inspiration. If the methods others use are too tricky, do not let yourself get stuck on one paper. For any particular topic, there are almost always many papers out there once you discover the hidden keywords to use (they’re most often in the introduction or background, and in the discussion). 

Pick a question and remember to follow the steps before proceeding with the actual statistical tests. Remember to report your statistics in the appropriate manner, including for your tests for assumptions and for the main test you’re running. Consult the Gries book for some basic forms you can borrow from if you need help with the style for reporting results. 

Other help: For help with plotting, use Quick-R ( Or, if you’re more adventurous, you could try ggplot2 ( which makes prettier plots than the standard R plotting tools.

Subject- civics

"Let's Get Controversial" 

It's time to talk about a real issue upon which our political parties have taken solid stands, and greatly oppose one another.

Instructions: You are to read the party platforms (their stand and position on a given topic), given below. Objectively read (that means to keep your personal opinion out of it!!!) the party stand on the issue of abortion. In a discussion board post, answer the following questions.

• Based upon their statements of belief, which political party's platform sounds most in favor of the population majority?

• What party sounds like it favors a smaller group of people?

• What party stand do you most favor?

• What party do you think is most distant from reality?

• What party's candidate would you most be inclined to vote for in a major election?

In all your responses, make sure you give an explanation why you've stated what you've posted.

MOST IMPORTANT!!!! Do not pass judgment, flame, or use derogatory expressions (don't "name call").

Here are the party platforms:

Republican Party on Abortion--Ban abortion with Constitutional amendment

We say the unborn child has a fundamental right to life. We support a human life amendment to the Constitution and we endorse legislation that the 14th Amendment’s protections apply to unborn children. Our purpose is to have legislative and judicial protection of that right against those who perform abortions. We oppose using public revenues for abortion and will not fund organizations which advocate it. We support the appointment of judges who respect the sanctity of innocent human life.
Source: Republican Platform adopted at GOP National Convention Aug 12, 2000
 

Democratic Party on Abortion--Choice is a fundamental, constitutional right

Democrats stand behind the right of every woman to choose. We believe it is a constitutional liberty. This year’s Supreme Court ruling show us that eliminating a woman’s right to choose is only one justice away. Our goal is to make abortion more rare, not more dangerous. We support contraceptive research, family planning, comprehensive family life education, and policies that support healthy childbearing.
Source: Democratic National Platform Aug 15, 2000

Green Party on Abortion

We believe the right of a woman to control her own body is inalienable. It is essential that the option of a safe, legal abortion remains available.
Source: Green Party Platform, at 2000 National Convention Jun 25, 2000

Libertarian Party on Abortion-Abortion is a woman’s choice and does not concern the state

Recognizing that abortion is a sensitive issue and that libertarians can hold good-faith views on both sides, we believe the government should be kept out of the question. We condemn state-funded abortions. It is particularly harsh to force someone who believes that abortion is murder to pay for another’s abortion. It is the right of the woman, not the state, to decide the desirability of prenatal testing, Caesarean births, fetal surgery, and/or home births.